THE CQF
CAREERS GUIDE
TO QUANTITATIVE
FINANCE
MAY 2023
Awarded By
Delivered By
CQF CAREERS GUIDE | CONTENTS | 2
THANK YOU TO OUR RECRUITMENT PARTNERS
CONTENTS
We would like to express our thanks to the companies across the finance sector that have collaborated with the CQF to
promote job opportunities to our alumni. Here is a selection of the companies we work with:
03
Introduction
04
The Increasing Demand for Quant
Professionals
08
Quantitative Finance Career Paths
34
Succeeding in Your Job Search
37
Career Opportunities
43
Conclusion
We also want to extend our gratitude to the recruitment companies that have contributed their expertise and time to
The CQF Careers Guide to Quantitative Finance 2023.
CQF CAREERS GUIDE | INTRODUCTION | 3
INTRODUCTION
Produced by the CQF Institute, The CQF Careers Guide
to Quantitative Finance 2023 is designed for people
who are seeking insights on the current state of the
industry, career opportunities, and skills needed in the
field of quant finance. The CQF Careers Guide examines
the quant landscape through the lens of recruiters, CQF
Institute members, and CQF alumni, who offer their
comments, stories, and survey responses to develop the
picture for quants today.
THE GUIDE COVERS SIX CAREER
PATHS IN QUANT FINANCE:
• Portfolio Management
• Risk Management
• Quant Strategies and Research
• Data Science and Machine Learning
• Technology
• Quant Trading
In each of these areas, this Guide will present a brief description
of the skills needed, typical roles and responsibilities, and general
salary ranges based on research from multiple sources including job
websites, recruiter interviews, and industry publications.
We look towards the future as well, with spotlights on growing areas
of career opportunity in machine learning and quantum computing.
This Guide provides a perspective on some of the best ways to
prepare for the job opportunities and industry changes that lie ahead.
This CQF Careers Guide also offers commentary on the value of
further education. In a highly competitive environment, financial
organizations are always seeking ways to apply innovative models
and methods to generate returns and manage risk effectively. This
requires skilled professionals with an understanding of finance,
mathematics, and programming. Delivered by Fitch Learning, the
Certificate in Quantitative Finance (CQF) is designed to meet this
need as a versatile professional designation, where delegates enhance
their knowledge and capabilities in each of these areas.
In creating this Guide, we spoke to a number of quant recruiters
and they all reported that 2022 was a year of increased demand
for quants across the industry. Looking ahead, we expect to see
continued demand for professionals with a strong quant skillset
across many functions within financial services. Those with strong
programming skills, expertise in machine learning, and an interest in
the ever-changing global financial landscape will be well-positioned
for job opportunities around the world.
As we celebrate the 20th
anniversary of the CQF
program, we are delighted
to present The CQF Careers
Guide to Quantitative Finance
2023 to support your journey
through the world of quant
finance, with real-world
alumni stories to showcase the
various roles within the field
and guidance on how you can
gain the skills you need for a
successful future career.
Dr. Randeep Gug, Managing Director,
CQF and CQF Institute
CQF CAREERS GUIDE | INCREASING DEMAND | 4
THE INCREASING DEMAND FOR
QUANT PROFESSIONALS
CQF CAREERS GUIDE | INCREASING DEMAND | 5
THE INCREASING DEMAND FOR QUANT PROFESSIONALS
The past few years have been turbulent times for
financial markets. Challenges include the COVID-19
pandemic, the war in Ukraine, pressure on energy
supplies in Europe, and rising inflation, which has driven
some central banks, including the Federal Reserve in
the US, to raise interest rates dramatically. Throughout
this period, quants have played an important role
in shaping investing and hedging strategies to meet
investors’ needs.
WHERE QUANTS WORK
Quantitative finance is a branch of investment management that
employs mathematical and statistical methods to analyze investment
opportunities across a range of asset classes. Practitioners in
quantitative finance work in equities, fixed income, structured
products, commodities, foreign exchange, and derivatives. Specific
areas include asset pricing, trading, hedging, portfolio analysis and
optimization, risk management, and regulatory compliance. Quants
are also increasingly involved in the world of artificial intelligence
and machine learning, where the employment opportunities for data
scientists have been growing dramatically.
The financial industry encompasses a wide range of organizations,
including investment banks, asset managers, hedge funds, prop trading
firms, insurance companies, technology firms, and consultancies. For
these types of employers, a key aspect for job candidates to consider
is the division between the “buy side” and the “sell side” – a distinction
that places emphasis on similar quantitative skillsets, but with
different objectives. Essentially, the buy side is comprised of mutual
funds, pension funds, foundations, endowments, and hedge funds
(institutional investors), as well as high-net-worth individuals. These
entities are focused on investing in securities and managing very large
funds or substantial individual or family resources, including those
overseen by private wealth managers and family offices.
In contrast, the sell side is comprised of investment banks, market
makers, and individuals who develop the products and services that
the buy side is seeking. This entails the creation, promotion, and sale
of stocks, bonds, currencies, commodities, derivatives, structured
products, and other financial instruments to the buy side and through
the public markets.
Outside of the investment banks and large asset managers, quants
can find roles in proprietary (“prop”) trading firms, where roles in
research and trading are prominent. At insurance companies, quants
are involved in portfolio and risk management, and within hightech firms and consultancies, roles lie in technology development,
data science research, and regulatory compliance. The emergence
of fintech has also created opportunities for quants, including
roles related to high-frequency trading, machine learning, and
cryptocurrencies, all of which require a strong quant skillset.
Read more for additional insights on these facets of the financial
services industry.
Industry Insights
In a poll conducted at the
CQF Institute’s Quant Insights
Conference in November 2022,
over 50% of all respondents
noted that one big challenge
facing quantitative finance
firms in 2023 would be
geopolitical instability.
CQF CAREERS GUIDE | INCREASING DEMAND | 6
THE INCREASING DEMAND FOR QUANT PROFESSIONALS
ESSENTIAL SKILLS IN
QUANTITATIVE FINANCE
Quants work in different areas of finance and the domain knowledge
varies for each specialization, but the essential skills for all quants draw
on the same foundations.
Core expertise for quants encompasses a solid grasp of mathematics
and modeling techniques, knowledge of probability and statistics, and
intermediate programming skills at a minimum. Quants are expected
to learn on the job, but having a general understanding of the financial
markets and demonstrating an interest in developing specific domain
expertise are important during the interview process.
Most of the people that we see usually have a
bachelor’s degree in a quantitative subject and
may have continued for a master’s degree as well.
Some have gone all the way through to a PhD.
They may be good at programming in Python,
C++, or R, depending on their role and what type
of business they are in. They are often enrolled in
or alumni of the CQF, which we actively promote
to our candidates since they can continue working
full-time while completing the program.
Patrick Flanagan, Clarence George
In brief, the essential knowledge domains for quant finance can
be described as follows:
Mathematical Skills – Quants draw on a variety of mathematical
methods, with a focus on probability, statistics, linear algebra, calculus,
and differential equations, including PDEs and SDEs, for pricing assets
from equities and bonds to structured products and derivatives.
Programming Skills – Programming skills have become essential
for quants. Traditional programming languages such as C and C++
have been popular for quants historically, and Python has become
prominent, especially for data science, in recent years.
Financial Skills – Even entry level quants should possess an
understanding about the various asset classes and financial instruments
available in the markets. Depending on their role within a financial
firm, a quant will need to develop detailed knowledge of asset
pricing techniques, trading methods, investment strategies, portfolio
management, or risk management practices.
Since each of these fields is quite complex, many people feel a need
to continue their education beyond a bachelor’s degree, or equivalent
undergraduate degree, and will often undertake programs in higher
education and professional qualifications to bridge the gap.
From a technical programming
perspective, Python is pretty
much everywhere at this point
in time. I’ve heard it referred
as the second-best language
for everything, which I think
is really testament to the
versatility of the language.
James Jarvis, Head of Research and Chair of
the Investment Committee, Trium Capital
CQF CAREERS GUIDE | INCREASING DEMAND | 7
THE INCREASING DEMAND FOR QUANT PROFESSIONALS
EMPLOYMENT TRENDS IN
QUANTITATIVE FINANCE
For many financial industry participants, the first half of 2022 was
quite active, with substantial hiring across banks, hedge funds, and
tech vendors. As the markets entered a period of volatility, the
demand for quants with expertise in credit, fixed income, and equity
markets has remained strong.
uring the previous years of the pandemic, there
D
was a big push for commodities quants. Now
we are seeing a demand for credit and fixed
income quants. Within the last six months, for
investment banks and hedge funds, the credit
risk space was the buzzword for obvious reasons
– across the global markets, interest rates have a
massive effect on credit and credit risk.
James Holland, Quant Capital
Reflecting on recent hiring trends, recruiters reported that in recent
years, there had been a significant demand for commodities quants.
Now they are seeing an increase in hiring of credit and fixed income
quants. In particular, credit risk has become a topic of interest for large
banks and hedge funds, as interest rate increases have a significant
effect on credit and credit risk throughout the global markets.
A second hiring theme expressed by many recruiters entails advanced
programming skills, with high demand for quant candidates who have
both a strong mathematical background and software development
experience. These trends are likely to continue, with an emphasis on
practical applications of the quant skillset.
Industry Insights
According to a poll conducted by the CQF Institute
at the Quant Insights Conference in November
2022, nearly 53% of respondents felt that the
biggest opportunity for quantitative finance firms
in 2023 will be in market volatility.
CQF CAREERS GUIDE | CAREER PATHS | 8
QUANTITATIVE FINANCE
CAREER PATHS
CQF CAREERS GUIDE | CAREER PATHS | 9
QUANTITATIVE FINANCE CAREER PATHS
This section of The CQF Careers Guide will outline six different career
paths in quantitative finance:
PORTFOLIO
MANAGEMENT
QUANT STRATEGIES
AND RESEARCH
RISK MANAGEMENT
DATA SCIENCE AND
MACHINE LEARNING
Each career path will present a brief description of the typical roles
and responsibilities, with specific examples by job title. The job
hierarchy is split into entry level, mid-level, and senior roles. Although
job hierarchies and related job titles vary from firm to firm, in general,
entry level is a designation for someone coming out of university
or with 0 to 5 years professional experience. A mid-level employee
would have 5 to 10 years of experience, and a senior level person
would have more than 10 years of work experience.
In addition, a member of the CQF alumni community working in
each quant finance career path outlines what a typical working day
looks like for them in terms of the tasks they complete and the skills
required for their roles.
We will now look at the six categories, describing the general attributes,
required skills, and typical compensation ranges for each role.
TECHNOLOGY
QUANT TRADING
Industry Insights
A Quant Insights Conference poll conducted
by the CQF Institute in November 2022 asked,
“Which quantitative finance career path will see
the most growth in job opportunities in 2023?”
The majority of respondents (over 54%) replied
that Data Science and Machine Learning were
the most promising in terms of growth. Quant
Strategies and Research came in second at
about 18%, with Risk Management (12%), and
Technology (11%) close for third.
CQF CAREERS GUIDE | CAREER PATHS | 10
PORTFOLIO MANAGEMENT
CQF CAREERS GUIDE | CAREER PATHS | 11
PORTFOLIO MANAGEMENT
Professionals working in portfolio management
are responsible for asset allocation and portfolio
construction. They initiate trades and monitor portfolios
and their exposures carefully.
SKILLS FOR PORTFOLIO
MANAGEMENT
Quants in portfolio management will have strong quantitative
and mathematical modeling, coding, and analytical thinking skills.
They have a deep understanding of the various asset classes and
a strong, clear communication style. They also tend to have good
people skills, as their role may entail direct interactions with clients,
which includes handling requests, observing pre-trade client guideline
compliance, and addressing tax and other management issues. They
must possess extensive knowledge of the firm’s investment products
as well as products that are available in the broader financial market.
Many people on this path begin their careers as portfolio analysts,
and some will progress to managing teams of analysts
and researchers.
Industry Insights
During a CQF Institute talk entitled, “A Day in
the Life of a Quantitative Portfolio Manager,”
by CQF alumnus Michael Althof (May
2022), respondents strongly agreed that the
portfolio manager of the future will require
more technical skills than he or she needs
today (over 68%).
CQF CAREERS GUIDE | CAREER PATHS | 12
PORTFOLIO MANAGEMENT
TYPICAL JOB AREAS
PORTFOLIO ANALYST
Portfolio analysts conduct in-depth portfolio analysis, encompassing
asset class and industry knowledge, insights on historic trends in the
markets, and an understanding of financial metrics and regulatory
and legal restrictions that may affect the portfolio. Portfolio analysts
communicate with portfolio managers, as well as trading, risk, and
compliance teams. They may also make presentations to clients.
QUANTITATIVE ANALYST
Quantitative analysts use a range of techniques to price assets,
manage risk, and identify investment opportunities. Quant analysts
will work in the front or middle offices at an investment firm, asset
manager, or hedge fund, with the front office being closer to the
clients and trading, and the middle office working on risk management
and model validation.
COMPENSATION (IN USD)
PORTFOLIO MANAGEMENT
INVESTMENT BANK
North America
Europe
Asia
Base
Total Comp
Base
Total Comp
Base
Total Comp
Portfolio Analyst
Associate
$175,000 $200,000
$220,000 $253,000
$90,000 $140,000
$100,000 $175,000
$95,000 $130,000
$135,000 $160,000
Portfolio Manager
VP
$200,000 $230,000
$275,000 $295,000
$140,000 $215,000
$160,000 $300,000
$130,000 $190,000
$225,000 $300,000
Portfolio Manager
$230,000 Senior VP / Director $300,000
$550,000 $700,000
$215,000 $300,000
$285,000 $530,000
$190,000 $250,000
$350,000 $450,000
QUANT PORTFOLIO MANAGER
CQF Corner
Quant portfolio managers focus on the use of quantitative investment
strategies to manage portfolios for institutional and retail investors.
They develop statistical and mathematical models to analyze empirical
data, searching for patterns and insights to inform the investment
decision-making process.
The CQF program gives delegates a strong
understanding of asset allocation and portfolio
construction, covering everything from modern
portfolio theory and the capital asset pricing model
to advanced portfolio management techniques.
CQF CAREERS GUIDE | CAREER PATHS | 13
A DAY IN THE LIFE OF A PORTFOLIO
MANAGER
Michael Althof, CQF alumnus, Portfolio Manager and Head of ETF
Capital Markets Team, Royalton Partners
8:00 AM - 9:00 AM
I arrive at the office, prior to market open. Overnight news is priced into
the most liquid instruments first before being priced into securities off
those core instruments. Only urgent trades will be undertaken prior to
market settling.
9:00 AM - 12:00 PM
Most of the adjustment trades have been done and a renewed Portfolio
Composition File (PCF) is sent to the ETF dealing community. Insights
from markets and policy makers are discussed in morning briefings.
Resulting adjustment trades are calculated for the required portfolios. The
only constant is the ongoing reevaluation of active portfolio positions. This
counts all the more for absolute return mandates with leveraged positions,
but even passive portfolios tracking a benchmark will need adjustments
from time to time. Trade flow optimization will be discussed.
12:00 PM - 13:00 PM
The dealer community in ETFs is informed by a refreshed PCF. The portfolio
manager’s task is to verify the correct screen pricing of shares in the fund in
secondary markets and to alert the market makers on deviations.
13:00 PM - 14:00 PM
This is usually when the US markets wake up. Coordination of positioning
into the macro data prints will be discussed. On the follow, market action
is analyzed and again the loop of market pricing levels and portfolio
composition is discussed.
14:00 PM - 16:00 PM
Time for portfolio committee meetings. Resulting new portfolio
compositions are taken back to the teams for adjustment trades to be
enacted over a given time period and with given market price limits.
16:00 PM - 17:00 PM
We are nearing the cutoff for the daily net asset value per unit or share
calculation. This is the primary market activity in a fund including ETFs. In
the case of the latter, authorized participants will come in with orders to
create or redeem shares, versus in-specie (basket of securities), or cash.
The pricing of the basket versus the number of shares is estimated into
market close and the final prices set at the security valuation going into
the NAV calculation. This needs close monitoring.
17:00 PM - 18:00 PM
Pricing is behind us and follow-on adjustments are enacted as long as
market liquidity can be found. Tickets traded during the day are checked if
passed through post-trade compliance, settlement issues are solved, and
market conformity checks are done.
18:00 PM - LATER
Time for reading, preparing presentations to teams, clients, newspapers, and
blog posts. The trade floor is calm now, and there’s space for deep thinking.
Read more about a day in the life of a portfolio manager.
Portfolio managers start early, as
overnight risk reports will have run and
positions need to be checked. There are
two viewpoints, granularly by security
level and individual risk measures,
and as a composition of a portfolio,
including the interdependencies across
the portfolio. Since values change with
market moves, they need to be watched,
adjusted, and checked constantly.
Michael Althof, CQF alumnus, Portfolio Manager and
Head of ETF Capital Markets Team, Royalton Partners
CQF CAREERS GUIDE | CAREER PATHS | 14
RISK MANAGEMENT
CQF CAREERS GUIDE | CAREER PATHS | 15
RISK MANAGEMENT
Professionals working in the risk management path
support the investment decision-making process
through risk analysis and the creation of risk model
frameworks for specific assets and asset classes.
SKILLS FOR RISK MANAGEMENT
Quants working in risk management possess strong quantitative
and financial modeling skills and have proficiency with programming
in Python, for example. They have knowledge of various methods
including “Value-at-Risk” (VaR and its variants), statistical models,
and simulations to evaluate the risk exposure for an asset or across
an entire portfolio of assets. They require knowledge of stochastic
calculus, Monte Carlo, PDEs, and other numerical techniques.
They need to have familiarity with financial markets, including the
most recent regulatory developments. Over the past decade or so,
there has been a strong emphasis on regulatory compliance and
stress testing and risk managers are often engaged in model testing
and validation. Quants in risk management tend to have good
communication skills and maintain focus on details and compliance.
Post-financial crisis, there is a lot of work
in risk management to be done with
models and LIBOR transition and handling
the regulatory aspects of the business,
doing testing and model validation, and
writing reports. The risk roles are quite
different from those on the trading
floor or in the front office, but there are
interesting challenges and problems to be
solved in this space as well.
John Meadowcroft, Anson McCade
Industry Insights
According to the CQF Institute’s Quant
Finance Careers Survey, for people in Risk
Management, over half of daily tasks (55%)
involve modeling, data analysis, and coding.
An additional 20% of their time is spent
on research and team management. The
remaining 25% of their time is spent on tasks
such as client interaction, market monitoring,
and strategic planning.
CQF CAREERS GUIDE | CAREER PATHS | 16
RISK MANAGEMENT
TYPICAL JOB AREAS
RISK ANALYST
A risk analyst evaluates individual assets, portfolios, and external
industry and economic conditions to help firms make risk-aware
investment decisions.
COMPENSATION (IN USD)
RISK MANAGEMENT
INVESTMENT BANK
North America
MARKET, LIQUIDITY, OR CREDIT RISK MANAGER
Risk managers use data analytics and mathematical models to
evaluate the risk profiles of financial instruments and portfolios,
measuring the changes to those profiles over time. They are
responsible for risk reporting internally to senior management and
externally to regulators.
MODEL VALIDATION QUANT
Model validators work with models and methods developed by front
office quants to assess their validity and mitigate the existence of
model risk. Since the Global Financial Crisis, regulators often interact
directly with quants in the middle office, including model validators.
This area of quant finance has grown significantly in recent years.
Europe
Asia
Base
Total Comp
Base
Total Comp
Base
Total Comp
Risk Analyst
Associate
$70,000 $90,000
$77,000 $99,000
$65,000 $80,000
$71,000 $88,000
$45,000 $60,000
$55,000 $66,000
Risk Manager
VP
$90,000 $150,000
$99,000 $165,000
$80,000 $130,000
$88,000 $143,000
$60,000 $125,000
$66,000 $132,000
$165,000 $243,000
$130,000 $160,000
$143,000 $176,000
$125,000 $150,000
$132,000 $165,000
Risk Manager
$150,000 Senior VP / Director $230,000
CQF Corner
The CQF program helps delegates build knowledge
of risk models and analytical practices and covers
a range of methods such as VaR and its variants,
Monte Carlo simulation, time series analysis, stress
testing, and statistical models.
CQF CAREERS GUIDE | CAREER PATHS | 17
A DAY IN THE LIFE OF A RISK
MANAGER
Bilardo De La Victoria, CQF alumnus, Market and Liquidity Risk
Manager, Superintendencia de Bancos de Panama
8:00 AM - 9:00 AM
I start my day logging into Bloomberg and Refinitiv to catch up with
the most important market news in Panama and the rest of the world,
especially news related to the banking industry and its regulators. I
also look for a summary of the behavior of the main market indicators
such as stock indexes and interest rates.
9:00 AM - 11:00 AM
At this time, I usually meet with my team members individually to
discuss ongoing projects. These projects may be of a diverse nature,
from bank inspections and evaluations, to the development of
regulatory requirements and standards, to the implementation of risk
metrics or models and their automation.
11:00 AM - 13:00 PM
I usually spend about two hours a day reviewing the work done by
the department’s risk analysts and inspectors. This work is generally
reflected in bank inspection reports, which contain a comprehensive
analysis of the bank’s models and practices to assess its risks and
value its investments, as well as the findings of the inspection.
14:00 PM - 16:00 PM
During this time, I often work on automation projects that aim to
implement indicators and models to monitor risks across all banks.
For this task I use SQL to query information from the databases. I also
use Python to process and transform the data, as well as to build risk
monitoring dashboards in a web-based application.
It is also very common to have meetings with the risk and treasury
departments of banks at this time. During these meetings, my team
and I have technical discussions about the methodologies that
the bank uses to measure its risks and value its investments and
derivatives positions. Other issues related to risk management are
addressed as well.
16:00 PM - 17:00 PM
I catch up with my supervisor on any matter of relevance to the
department and to find out if there is anything that needs to
be addressed. I brief the analyst team on the highest priority
issues and invite them to discuss any improvement initiatives or
recommendations they may have developed. I also take advantage of
this time in the day to study any technical topics that I need to face
new challenges at work.
17:00 PM - 18:00 PM
My working day usually ends by this time. Before I leave the office,
I go through my emails and to-dos and make note of the most
important things for the next day.
Read more about a day in the life of a risk manager.
I am responsible for the team that
evaluates all banks in the Panamanian
Banking System in relation to their
practices for measurement and
management of market risk, liquidity
risk, and interest rate risk in the banking
book. In this role, I have participated in
the development and implementation
of rules for the banking sector, such as
capital requirements for market risk,
investment management, financial
derivatives, liquidity risk management,
and the liquidity coverage ratio.
Bilardo De La Victoria, CQF alumnus, Market and
Liquidity Risk Manager, Superintendencia de Bancos
de Panama
CQF CAREERS GUIDE | CAREER PATHS | 18
QUANT STRATEGIES
AND RESEARCH
CQF CAREERS GUIDE | CAREER PATHS | 19
QUANT STRATEGIES AND RESEARCH
Professionals working in quant strategies and research
often use quantitative and statistical methods to
analyze the markets, and then generate and test ideas
for investment strategies. These quants focus on
mathematical models, with the potential to generate
alpha, while also managing risk effectively.
SKILLS FOR QUANT STRATEGIES
AND RESEARCH
Quants working in strategies and research will have a detailed
knowledge of mathematical and statistical models used in quant
finance. They also require knowledge of financial mathematics and
stochastic calculus. They will have good programming skills in Python
or C++, for example, and may have skills in R, MATLAB, or SAS as
well. Knowledge of machine learning and natural language processing
techniques is increasingly in demand for quant research and analysis.
Over the past two years we’ve seen
a significant uptick in quant macro
– systematic macro strategies, and
portfolio managers or trading teams that
are looking at cross-asset futures, FX,
commodities – any products that are
driven by global macroeconomic events
and situations. Even the more traditional
equity players are starting to diversify and
grow their teams in the macro space.
Tyler Robinson, Selby Jennings
CQF CAREERS GUIDE | CAREER PATHS | 20
QUANT STRATEGIES AND RESEARCH
TYPICAL JOB AREAS
QUANT RESEARCHER / QUANT ADVISOR
Quant researchers or quant advisors develop and implement
pricing models and trading strategies and analyze existing strategies
to identify potential improvements. They also create tools to
automate research tasks and visualize the information found in
complex data sets. Responsibilities may include working on strategy
research, backtesting models, execution, latency strategy research,
machine learning research, econometrics research, and market
microstructure research.
QUANT STRATEGIST
Quant strategists research and implement trading strategies, using
pricing and trading models. They also develop risk models to
manage portfolio risks and analyze current strategies to identify
issues and make improvements. Quant strategists often work with
traders, quant analysts, software engineers, and quant developers.
Responsibilities include analyzing trading and asset allocation
opportunities and working with a comprehensive set of risk
reporting and pricing tools.
DERIVATIVES ANALYST
Derivatives analysts apply mathematical formulas and computer
algorithms to evaluate financial data, detect investment trends,
and recommend asset allocation strategies. They may also evaluate
transactions from risk management and legal standpoints to ensure
compliance with regulatory requirements.
COMPENSATION (IN USD)
QUANT STRATEGIES AND RESEARCH
BUY SIDE
North America
Europe
Asia
Base
Total Comp
Base
Total Comp
Base
Total Comp
Quant Researcher
Associate
$145,000 $165,000
$250,000 $300,000
$100,000 $130,000
$175,000 $300,000
$95,000 $125,000
$165,000 $185,000
Quant Researcher
VP
$165,000 $190,000
$300,000 $500,000
$130,000 $180,000
$300,000 $500,000
$125,000 $180,000
$185,000 $275,000
Quant Researcher
$190,000 Senior VP / Director $300,000
$500,000 $700,000
$180,000 $250,000
$500,000 $650,000
$180,000 $250,000
$275,000 $450,000
CQF Corner
The CQF program gives delegates a strong
understanding of the mathematical and statistical
models, and machine learning techniques needed
to work in quant strategies and research.
CQF CAREERS GUIDE | CAREER PATHS | 21
A DAY IN THE LIFE OF A QUANT
ADVISOR
Karolina Hartzell, CQF delegate, Quant Advisor, Pexapark
8:00 AM - 9:00 AM
Usually, I tend to start work around 8:00am and I begin by replying
to any remaining messages that I may not have managed to answer
the previous day.
9:00 AM - 13:30 PM
I start my day by prioritizing tasks. I tend to focus first on any type of
assignment, which has been requested directly by the client.
A recent example of such an analysis involved running a Monte Carlo
simulation of the main risks affecting an offshore wind investment in
Germany and reviewing how different types of contract structures
(hedging instrument) can help to offset the risks and at what cost.
I use our simulation engine to obtain thousands of scenarios of
realizations of the risks and use self-designed code to calculate payoffs for any given complex structure or strategy.
Once the analysis part is done, my task is also to help interpret
the results and create a clear and easy to understand message for
my clients. I usually work with our Advisory team who are market
experts and keep close contact with the client to manage the
delivery of the results.
14:15 PM - 16:00 PM
I have a few calls with my colleagues, with whom I work on the
different assignments. It’s always good to quickly debrief on the
progress of any analysis work or any challenges in the way of
completion. If the quantification part of the project is already done,
we work together on the presentation of the results.
16:00 PM - 17:30 PM
If I have completed all my prioritized tasks, I use this time to work
on the development projects that aim to expand our software. There
are still a lot of interesting things to do when it comes to helping our
customers better understand and optimize their investment portfolios
containing renewable assets. These projects often involve many
stakeholders and require detailed interaction and alignment with our
Quant Engineering team. After this, I am usually done for the day.
Read more about a day in the life of a quant advisor.
I am involved in many different projects
across the company, which means I get
requests for various types of analysis.
A recent example involved running a
Monte Carlo simulation of the main risks
affecting an offshore wind investment
in Germany and reviewing how different
types of hedging instruments can help
to offset the risks and at what cost. I
use our simulation engine to obtain
thousands of scenarios of realizations of
the risks and use self-designed code to
calculate pay-offs for any given complex
structure or strategy.
Karolina Hartzell, CQF delegate, Quant Advisor,
Pexapark
CQF CAREERS GUIDE
CQF |CAREERS
CAREER PATHS
GUIDE | 22
DATA SCIENCE AND
MACHINE LEARNING
CQF CAREERS GUIDE | CAREER PATHS | 23
DATA SCIENCE AND MACHINE LEARNING
Professionals working in data science and machine
learning are responsible for research, modeling,
and testing. They work with data sets to uncover
relationships and patterns in empirical data.
SKILLS FOR DATA SCIENCE AND
MACHINE LEARNING
Professionals working in this area need to have a deep understanding
of algorithms, machine learning, and specific domains such as natural
language or signal processing to help identify and assess patterns
in the data. They have strong quantitative analysis skills and a
solid understanding of artificial intelligence and machine learning
techniques, as well as familiarity with the programming languages
commonly used in machine learning, particularly Python.
Roles for quants in data science and machine learning require
significant knowledge of models and programming. These jobs
tend to sit within the research area of an organization. Firms that
are active in data science and machine learning include investment
banks, asset managers, hedge funds, and technology companies
that offer consulting services to the financial industry. There are also
opportunities for quants in tech organizations that develop software
products for the financial industry.
Many firms are hiring people who have
great technical skills. Those who can write
good code and are truly hands-on with
programming are in demand. In terms of
languages, Python is now the industry
standard for data analysis and machine
learning. It’s an important skill to have,
along with a good knowledge base in math,
statistics, probability, and game theory.
Tyler Robinson, Selby Jennings
Industry Insights
In the November 2022 Quant Insights
Conference Polls, attendees reflected on the
most important skills for quants working in
Data Science and Machine Learning. Data
Analysis (about 38%) and Modeling (about
34%) far outpaced Coding (about 19%) and
Research (about 9%), as seen by the poll
respondents.
CQF CAREERS GUIDE | CAREER PATHS | 24
DATA SCIENCE AND MACHINE LEARNING
TYPICAL JOB AREAS
DATA SCIENTIST
Data scientists in quantitative finance apply their analytical skills
to extract insights from large datasets, using machine learning
algorithms and statistical methods to inform decision-making and
drive business strategies.
MACHINE LEARNING ENGINEER
Machine learning engineers focus on building, training, and
deploying machine learning models tailored to the needs of
financial institutions, ranging from predictive analytics to natural
language processing.
DATA ENGINEER
Data engineers build systems that collect, manage, validate, and
convert raw data into high-quality, usable information for data
scientists to study. In quant finance, this data would include
information from stock exchanges, the OTC markets, and other
market, trading, and business information.
DATA ANALYST
Data analysts use descriptive statistics to evaluate problems,
create data visualizations, and develop insights based on empirical
analysis. They may assist with collecting and cleaning data sets and
supporting the senior members of the data science team.
COMPENSATION (IN USD)
DATA SCIENCE AND MACHINE LEARNING
INVESTMENT BANK
North America
Europe
Asia
Base
Total Comp
Base
Total Comp
Base
Total Comp
Data Analyst
Associate
$75,000 $150,000
$85,000 $165,000
$70,000 $100,000
$80,000 $110,000
$80,000 $110,000
$90,000 $120,000
Data Scientist
VP
$150,000 $230,000
$165,000 $250,000
$100,000 $180,000
$110,000 $200,000
$110,000 $130,000
$120,000 $140,000
Data Scientist
Senior VP/Director
$230,000 $260,000
$250,000 $285,000
$180,000 $250,000
$200,000 $275,000
$130,000 $200,000
$140,000 $220,000
CQF Corner
With modules on Data Science and Machine Learning, the
CQF program gives delegates the experience of using modeling
and machine learning methods to solve real-world problems
in finance. Python Labs allow delegates the opportunity to
implement the models and techniques studied in lectures.
CQF CAREERS GUIDE | CAREER PATHS | 25
A DAY IN THE LIFE OF A LEAD
DATA SCIENTIST
Victor Acevado, CQF alumnus, Lead Data Scientist,
Banco de Credito del Peru
8:30 AM - 9:00 AM
I start work with a quick review of my emails and meetings for the
day. I also check the sticky notes I have on my desktop with the
messages I would like to give the team throughout the day.
9:00 AM - 9:30 AM
Time for a daily meeting to coordinate with the team. I usually ask for
a quick summary of the previous day’s progress and about any issues
that may be holding up the team’s progress to see how I can help.
9:30 AM - 12:30 PM
During this time, users will sometimes call me to ask about the
models we develop. The analytical solutions that we provide to the
business units are wide-ranging. They can go from mitigating the risk
in specific sectors of the population to generating relevant offers for
a target audience. However, the majority of our work is focused on
developing models for the prediction of credit risk.
We often discuss with our users the type of algorithm that should be
used, the main assumptions, how the solution can be deployed, and
the development time.
14:00 PM - 17:30 PM
I continue working on the proposed solutions to business problems
and
then present them to the users. I usually meet with the team at some
point in the afternoon in case they need my help.
17:30 PM - 18:00 PM
I review my emails one last time and write down list of things to start
or continue the next day.
Read more about a day in the life of a lead data scientist.
As Lead Data Scientist, I am responsible
for deploying and implementing
analytical solutions for credit risk
problems. Most of the time, the solution
involves estimating inputs for expected
loss, namely probability of default, loss
given default, and exposure at default.
We also build through the cycle version
of these parameters as needed for
economic capital requirements. Lately, I
have been dedicating much of my time
to designing a new workflow for building
a loss given default model for my
business segment.
Victor Acevado, CQF alumnus,
Lead Data Scientist, Banco de Credito del Peru
CQF CAREERS GUIDE | CAREER PATHS | 26
TECHNOLOGY
CQF CAREERS GUIDE | CAREER PATHS | 27
TECHNOLOGY
Quant professionals working in technology design,
develop, and implement software solutions to support
various departments across the firm.
SKILLS FOR TECHNOLOGY
Quants in technology will have excellent coding skills in Python, C,
C++, or C#, for example. They should also have a good understanding
of computational mathematics, software engineering, and financial
products. They tend to work on projects with a number of teams if they
are in a large organization, so having domain expertise combined with
good skills in collaboration and communication will be helpful.
Candidates who come in with strong
computer science backgrounds, including
natural language processing can do
very well in quant jobs now. If we single
out high-frequency trading firms, their
business method is about speed and
efficiency and the quants they employ
are skilled in using C++ and working with
ultra-low latency systems. Python is a
very useful and popular language as well.
Once you understand one type of coding,
it is fairly easy to pick up other languages
as needed.
John Meadowcroft, Anson McCade
CQF CAREERS GUIDE | CAREER PATHS | 28
TECHNOLOGY
TYPICAL JOB AREAS
QUANT DEVELOPER
Quantitative developers, also known as quantitative software
engineers, or quantitative engineers, develop, implement, and
maintain quantitative models. They are highly skilled programmers,
specialized in languages like Python or C, C++, and its variants, and
they often work at the intersection between software engineers
and quantitative analysts. Typical responsibilities may include
developing and maintaining programming libraries, developing
high-performance numerical library components, performance
tuning of libraries, and consulting on high-performance computing,
optimization, and strategy.
COMPENSATION (IN USD)
TECHNOLOGY
North America
Europe
Asia
Base
Total Comp
Base
Total Comp
Base
Total Comp
Quant Developer
Associate
$130,000 $150,000
$132,000 $154,000
$85,000 $120,000
$94,000 $140,000
$45,000 $60,000
$50,000 $66,000
Quant Developer
VP
$150,000 $220,000
$154,000 $260,000
$120,000 $155,000
$140,000 $170,000
$60,000 $121,000
$66,000 $133,000
Quant Developer
Senior VP/Director
$220,000 $260,000
$260,000 $300,000
$155,000 $170,000
$170,000 $187,000
$121,000 $153,000
$133,000 $168,000
CQF Corner
With online Python Labs, as well as advanced electives on C++
and Decentralized Finance Technologies, the CQF program helps
delegates develop excellent coding skills in Python so that they
are able to build, implement, and analyze quantitative models used
in technology roles. The CQF program also contains lectures on
quantum computing, one of the latest additions to the curriculum.
CQF CAREERS GUIDE | CAREER PATHS | 29
A DAY IN THE LIFE OF A LEAD
QUANT DEVELOPER
Alok Jadhav, CQF alumnus, Quant Developer, y-intercept
9:00 AM - 10:00 AM
I attend a daily stand up meeting with my team. During this meeting
we review and update the status of work done the previous day and
plan the day’s tasks. After the meeting, I review my items for the day
and get started. By 9:30am, I am completely focused on the tasks I
have at hand. Usually, I have one main task for the day or a few minor
tasks. I typically use morning hours to do the development work and
post-lunch hours are used for meetings and research.
10:00 AM - 12:15 PM
I keep working through my daily tasks. For example, one day I noticed
that one of the equity index futures didn’t roll automatically in EMEA.
After investigation I discovered that we had an exceptionally large
quantity to trade the previous day and the trading couldn’t complete
automatically due to constraints. I notified the team about the cause
of incompletion and asked the users to roll the remaining quantity
manually. With no more pending issues, I then focused my efforts on
the Algo Engine development.
12:15 PM - 13:00 PM
I continue to work on the Algo Engine development. It’s a big project and
still in the early stages. We use a JIRA board for tracking our deliverables.
For development, we follow the TDD approach, where you write the
test cases before you write the actual code. Currently, I am setting up
the Algo Engine in backtesting mode, which will aid in doing further
development and enable us to check the execution performance.
13:00 PM - 17:00 PM
I research new financial papers that could be useful for the Algo
Engine and then continue to work on development - closing those
Jira tasks one by one. If there were any issues reported with any
of them, I resolve those issues and plan for a release. Some issues
are urgent and require an urgent patch and release without waiting,
whereas other issues can be aligned in the next release of the
application.
17:00 PM - 18:00 PM
I catch up with other team members on issues pertaining to my
projects and schedule follow-up meetings with other teams. I also use
this time to catch up with the data team on any data requests. I then
have a status meeting with the management team, where we discuss
the longer-term plans for the execution platform.
Read more about a day in the life of a quant developer.
I am responsible for the execution
platform for the trading desk. This
platform receives target portfolios from
a quantitative trading team that need to
be executed over the day. Some orders
are meant to be forwarded to broker
algos, while others are to be executed
internally in a smart way, with the aim
of reducing trading costs. Lately, most
of my time is spent on a new application
called the Algo Engine, which is in the
very early stages of development.
Alok Jadhav, CQF alumnus, Quant Developer,
y-intercept
CQF CAREERS GUIDE | CAREER PATHS | 30
QUANT TRADING
CQF CAREERS GUIDE | CAREER PATHS | 31
QUANT TRADING
Professionals working in quant trading employ
mathematical and statistical models to identify
potentially profitable trading strategies and to execute
trades. They develop strategies and then focus on
backtesting, analysis, and optimization. Quant traders
may be involved in statistical arbitrage, algorithmic
trading, and high-frequency trading.
SKILLS FOR QUANT TRADING
Quant traders must have deep knowledge of quantitative and
statistical analysis, and strong programming skills in Python or C++,
for example. They may have experience with machine learning
techniques as well. Psychology is very important for quant traders and
trading job candidates must demonstrate that they thrive in extremely
competitive environments and can handle pressure well.
Employers are increasingly interested in
people who are not just technically and
quantitatively equipped. They must also
be knowledgeable and passionate about
markets and asset classes. They’re looking
for someone who has an interdisciplinary
understanding of how different factors
affect markets and can apply technical
knowledge to their investing activities
with intuition. The CQF definitely
provides an edge here, covering both the
financial acumen and the technical skills
to match.
Dennis Grady, Spire Search Partners
CQF CAREERS GUIDE | CAREER PATHS | 32
QUANT TRADING
TYPICAL JOB AREAS
QUANT TRADER
Quant traders trade a variety of asset classes, including equities,
bonds, commodities, currencies, and derivatives using a
combination of market knowledge, trading experience, and math
and computer skills. Quant traders work at investment firms, hedge
funds, and banks. They may also be proprietary (“prop”) traders
working in small groups within such organizations, or independently
for their own accounts.
COMPENSATION (IN USD)
TRADING
North America
Europe
Asia
Base
Total Comp
Base
Total Comp
Base
Total Comp
Quant Trader
Junior Trader
$100,000 $150,000
$125,000 $170,000
$100,000 $130,000
$140,000 $205,000
$70,000 $120,000
$120,000 $225,000
Quant Trader
Senior Trader
$150,000 $200,000
30-50% of PnL
$90,000 $145,000
10-40% of PnL
$150,000 $170,000
10-40% of PnL
Quant Trader
Head of Trading
$240,000 $300,000
$550,000 $700,000
$210,000 $290,000
$260,000 $525,000
$215,000 $250,000
$325,000 $375,000
CQF Corner
The CQF program teaches the mathematical
models traders need to price assets, manage risk,
predict market movements, implement algo trading
strategies, and find arbitrage opportunities.
CQF CAREERS GUIDE | CAREER PATHS | 33
A DAY IN THE LIFE OF A LEAD
QUANT TRADER
Vitor Angrisani, CQF alumnus, Quantitative Equity Trader, RBC
Global Asset Management
7:00 AM - 9:30 AM
First, I check my live orders from Europe (which are in mid-trading
session at this time), then I check overnight fills from APAC and make
sure I am up to speed and comfortable with the trading strategy in
place, or adjust as needed. Once all overseas orders are under control,
I move on to the “pre-North America open” phase of the day.
Before the open of the North American market, it is important to go
through the news and evaluate any relevant macro or stock-specific
events that might impact my trading day. Pre-market preparation
includes reading reports, taking calls from brokers, strategizing and
implementing trading strategies on the new and multi-day equity
orders on my blotter, and sharing the highlights with the Quant
Portfolio Managers and my trading peers.
9:30 AM - 12:00 PM
At 9:30am, the North America market opens and dominates my
attention. As a trader, your primary responsibility is to execute orders
while keeping market impact to a minimum. In order to do this, we use
a variety of systems and tactics, including algorithmic and block trading.
I also analyze and present relevant trade opportunities based on the
activity of other market participants to Portfolio Managers, bringing
insights from the market to complement the signals from our models.
12:00 PM - 16:00 PM
No lunchtime for traders. You can’t afford to be away from the desk
if something happens. Your reaction time to any adverse or favorable
price action has direct P&L implications and mere seconds can really
cause a multi-million loss or gain, so you have to be connected at
all times. Every moment away from the desk has to be covered by a
backup trader.
16:00 PM
After all markets close at 16:00pm, I book my trades and catch up
with any unread emails before leaving for the day. This is also the
time to safely work on any side projects without being interrupted. I
usually work on backtesting new strategies.
Read more about a day in the life of a quant trader.
I started my career in 2008, right before
the Great Financial Crisis, and had a
chance to experience first-hand its impact
in financial markets. Since then, I have
worked in different roles and companies,
managing and taking risk, in both the sell
side and buy side. Once I began trading
derivatives, I decided to enroll in the
CQF program, as it was the best way to
acquire the essential quant skills needed
to progress in my career.
Vitor Angrisani, CQF alumnus, Quantitative Equity
Trader, RBC Global Asset Management
CQF CAREERS GUIDE | JOB SEARCH | 34
SUCCEEDING IN YOUR JOB SEARCH
CQF CAREERS GUIDE | JOB SEARCH | 35
SUCCEEDING IN YOUR JOB SEARCH
As we have seen, in each of the six job categories discussed, there
is a strong emphasis on quantitative and analytical skills, technical
expertise, and knowledge of specialized areas of finance. Most roles
require strong communication skills and entail a significant amount
of interaction with internal and external clients. A challenging and
satisfying career in quant finance depends on the ability to adapt
to changing conditions in the financial markets and the desire
to improve on one’s skillset and perspective continuously. The
following section addresses additional considerations when seeking
a role in quant finance.
INTERVIEW PREPARATION
Since quant finance is an intellectually demanding field, employers
will typically test a candidate’s knowledge and skills quite rigorously
throughout the interview process. Financial problems, math
brainteasers, and programming samples are often part of the journey.
When preparing for an interview, it is good practice to review your
knowledge and skills, study the types of questions likely to be posed
to you, and research the company carefully. In addition, you may wish
to look up the person with whom you will be interviewing on the
firm’s website and LinkedIn. The details on their academic background
and career progression can provide clues on how they will see the
world and what types of knowledge they may explore with you.
Ahead of the interview, prepare for various types of conversations.
A few good resources for quant interviews include Heard on The
Street: Quantitative Questions from Wall Street Job Interviews, by
Timothy Falcon Crack, Quant Job Interview Questions and Answers,
by Mark S. Joshi, Nick Denson, and Andrew Downes, 150 Most
Frequently Asked Questions on Quant Interviews, by Dan Stefanica,
Radoš Radoicić, and Tai-Ho Wang, and Frequently Asked Questions in
Quantitative Finance, by Paul Wilmott.
BUSINESS SKILLS:
COMMUNICATION, CURIOSITY,
AND COLLABORATION
In order to advance through the ranks of either a financial or a
technology-focused firm, an employee needs to develop domain
expertise and demonstrate consistent high-quality performance.
Recruiters also advise that business skills such as communication
(both verbal and written), collaboration, and empathy become
increasingly important when working on larger teams or moving into
managerial roles. A quant can be a strong individual contributor and
highly effective at managing projects, which can also lead to career
advancement. Those who are able to manage people well may move
into senior executive roles. One of the ways to help ensure good
career progression is to hone your communication skills – be able
to present ideas and explain them clearly to other quants and to
colleagues in non-technical roles.
For quants, typically you have a great
undergraduate degree and perhaps a
postgraduate degree, and ideally, you’ve got
some professional qualifications such as the
CQF. This is maybe 80% along the way to
being a great job candidate, but the last 20%,
is up to you. For example, when it comes to
programming, it’s not necessarily about being
the best at a specific skill, it’s about showing
progression and aptitude.
Richard Booty, Testwood Partners
CQF CAREERS GUIDE | JOB SEARCH | 36
SUCCEEDING IN YOUR JOB SEARCH
Recruiters also advise that employers value curiosity; they would
like to know what you have done outside of your studies. Focus on
demonstrating your interest in quantitative subjects, finance, and
programming. Many recruiters note that in the job interview process,
it’s useful to have completed a significant technical project - something
you have developed and can talk about in detail. It’s not necessarily
important that the project was completely successful; you can talk
about things you have learned from a particular challenge or obstacle.
Another good topic for interviews is the books you have been
reading lately; be prepared to offer analysis of key points from the
readings. It shows that you can assimilate information and put it to
use. Recruiters were unanimous on the point that showing a different
perspective and curiosity about the world can be a significant factor
in job search success.
CQF Corner
All delegates on the CQF program are required
to complete a practical final project before they
graduate, pushing them to apply their new skills
to a real-world financial scenario. Many alumni
state that their final project was directly applicable
to their career development and that they often
refer to this project during interviews following
completion of the program.
Finally, it is very helpful to become part of a professional
organization that is active in the financial world. Such memberships
show commitment to potential employers and provide access
to and motivation for additional education on the industry. The
CQF Institute offers numerous benefits to its members, including
lectures, conferences, and other educational opportunities in
quantitative finance.
nderstanding the principles of model
U
development, data structures, and automation
is essential for today’s quant jobs. The CQF has
modules that are focused on precisely these
areas, so it is addressing the current trends in
the industry.
Lee Horan, Rec Finance
THE VALUE OF NETWORKING
Beyond developing your professional connections online, many
quant finance conferences are held in major financial centers around
the world and they offer good networking opportunities as well.
The CQF Institute, for example, holds a series of conferences and
lectures throughout the year, including the annual Quant Insights
Conferences, the Career Insights lecture series, and other industry
talks in various locations. In addition to professional certification
courses like the CQF, such events are an excellent way to learn about
trends in the job market and explore new opportunities for people
with a strong quantitative skillset.
A final note on career progression entails nurturing your network.
Social media including LinkedIn and WhatsApp make it easy to stay
in touch with classmates and colleagues. CQF alumni can showcase
their CQF designation in several ways on profile pages, from the
headline to the education and professional credential sections, so
that they are more easily searchable on such platforms.
Industry Insights
Respondents to the Quant Finance Careers
Survey conducted by the CQF Institute concur:
76% felt that a combination of communication,
collaboration, and leadership were most
important for career progression in finance,
as opposed to favoring a single one of those
options over the others. Further, 74% stated that
networking plays an important or very important
role in developing a career in quant finance.
CQF CAREERS GUIDE | CAREER OPPORTUNITIES | 37
CAREER OPPORTUNITIES
CQF CAREERS GUIDE | CAREER OPPORTUNITIES | 38
CAREER OPPORTUNITIES: GROWTH AREAS
Looking across the financial industry, some of the key
themes in quant finance remain the same as they have
been for decades, while others are evolving in response
to technological change. In keeping with the times, there
are growing career opportunities in both established and
emerging areas for quants, including machine learning,
data science, and quantum computing. With regard to
the competencies required in these fields, recruiters
emphasize the importance of upskilling and the value of
a good quant education, no matter what corner of the
quant world you are exploring.
Industry Insights
According to a poll conducted by the CQF Institute
at the Quant Insights Conference (November
2022), approximately 62% of respondents
indicated that Data Science and Machine Learning
would offer the greatest increase in career
opportunities in quantitative finance in 2023 and
17% felt that the greatest increase would come
from Quantum Computing.
The following sections offer a deeper look into the field of machine
learning and data science, as an area of particularly high interest for
quant professionals, and into the field of quantum computing, where
demand for quants is expected to grow in the future.
MACHINE LEARNING AND
DATA SCIENCE
Over the past decade, interest in machine learning has risen rapidly
across the financial industry, encompassing all aspects of the
industry. For quant finance professionals this provides a range of new
career opportunities.
How is machine learning used in
quantitative finance?
Machine learning is a branch of artificial intelligence that draws on
techniques from computer science and statistical modeling. Within
the world of quantitative finance, machine learning techniques enable
quants to discern patterns in large datasets, allowing them to make
more accurate predictions of market movements. These techniques
also enable the development of more sophisticated trading and
risk management strategies, which can result in improved portfolio
performance and reduced exposure to market volatility. Quants can
also automate complex financial processes with machine learning,
which enables faster and more competitive decision-making. As a result
of this, traditional investment strategies are beginning to evolve as the
markets begin to be shaped more and more by these techniques.
Industry Insights
According to a series of polls conducted at a
CQF Institute talk on Reinforcement Learning
by Samit Ahlawat (April 2022), respondents
stated that the most common machine learning
techniques that their firms had incorporated
regularly were Supervised Learning techniques
(at about 28%), but an equal percentage
responded that their firms had not incorporated
any machine learning techniques at all yet.
About 16% replied that their firms had adopted
Unsupervised Learning techniques, followed by
Reinforcement Learning (about 14%), and Deep
Learning (about 11%). Neural Nets came in last,
with about 3% of the vote.
With the advent of new AI-powered platforms, such as ChatGPT,
at the end of last year, this field is set to change even further.
With their natural language processing capabilities, tools such as
ChatGPT can help answer complex financial questions, analyze large
amounts of financial data, create predictive models, suggest new
strategies, and even generate Python code to solve specific problems.
Its range of capabilities make it a valuable tool for those in quant
finance seeking to improve their performance and enhance their
decision-making processes.
CQF CAREERS GUIDE | CAREER OPPORTUNITIES | 39
CAREER OPPORTUNITIES: GROWTH AREAS
Industry Insights
In a poll at the March 2023 CQF Institute Portfolio
Management in Quant Finance Conference, 64%
agreed that ChatGPT and similar applications will
change the nature of quant portfolio management.
Skills for machine learning and data science
As the markets continue to change with the emergence of new
machine learning developments, ambitious quants must ensure they
have the skills needed to stay competitive. Essential machine learning
quant finance skills include:
Mathematics: All quants need a solid understanding of core
mathematical concepts, but machine learning quants need this
knowledge to create and evaluate sophisticated machine learning models
and to understand the vast datasets used by machine learning tools.
Programming: Proficiency in a programming language, like Python,
is essential for machine learning quants as this facilitates the
implementation of machine learning algorithms, data manipulation,
and visualization.
Machine Learning Techniques: Quants need to understand the
different machine learning techniques, including supervised and
unsupervised learning, reinforcement learning, and deep learning.
Understanding the limitations of each of these methods enables
quants to choose the best approach for a given task.
Model Selection: This is a vital skill to ensure that a quant’s
predictions are robust. Quants need to be able to evaluate the
performance of machine learning models using various techniques
such as, cross-validation, confusion matrices, and ROC curves, and
should understand common pitfalls like overfitting and underfitting.
AI/ML Explainability Specialist: As machine learning models become
more complex and ubiquitous, explainability is a key concern for many
organizations, regulators, and senior executives. Quants in this role
develop methods to interpret and communicate the inner workings of
AI/ML models to ensure transparency, trust, and regulatory compliance.
Data Management: As the volume and variety of financial data
continues to grow, quants need to be adept at cleaning data to ensure
its quality. Expertise in data management systems, like SQL, is also
important as this allows quants to store and manipulate large datasets.
Crypto Quant: The rise of cryptocurrencies and blockchain technology
has created new opportunities for quants specializing in digital assets.
These professionals develop trading strategies, risk models, and
valuation methods specifically for the cryptocurrency market.
Like all career paths in quant finance, communication skills and
domain knowledge are vital to ensure quants can understand
the products they are dealing with and be able to communicate
complex machine learning concepts to less technical stakeholders.
A commitment to continuous learning is also important. As new
technologies continue to emerge in the field, quants need to ensure
their knowledge stays up to date throughout their careers.
These are just a few of the new job titles emerging for quants in the
field. For those quants that have the right skills, the opportunities for
career development and progression in machine learning and data
science could be boundless.
New career opportunities in machine
learning and data science
As machine learning becomes more prolific across the industry, new
roles for quants are beginning to emerge in the field. These include:
Alternative Data Analyst: With non-traditional data sources (such
as social media, satellite images, and web scraping) becoming
more important, quants in this role focus on extracting insights
from alternative data to inform investment strategies and improve
decision-making.
CQF Corner
As data science has gained traction in the
investment world, the CQF curriculum has
been updated to provide a strong foundation in
the core principles and techniques of machine
learning, including supervised, unsupervised, and
reinforcement learning, deep learning, neural nets,
NLP, and algorithmic trading.
CQF CAREERS GUIDE | CAREER OPPORTUNITIES | 40
CAREER OPPORTUNITIES: GROWTH AREAS
QUANTUM COMPUTING
It’s a similar story for quantum computing. The field of quantitative
finance began during an age of classical computers and it was both
influenced and limited by the technology available at the time. Since
then, computers have become ever more powerful, with greater
storage capacity and faster networks.
Quantum computing is an emerging form of high-performance
computing. It uses the principles of quantum mechanics to perform
computations significantly faster than classical computers. For
quantitative finance, this creates vast potential for processing complex
financial models and algorithms at unprecedented speeds, leading to
faster decision-making, portfolio optimization, and risk management.
Whilst investment in quantum computing is costly, interest in it is
growing and pioneering firms are making investments in quantum
technology as part of their corporate strategy. For example, research
initiatives are underway at Goldman Sachs and JP Morgan and many
start-ups and established firms, like IBM, are working to develop this
technology for practical application.
Industry Insights
Industry Insights
In a poll conducted by the CQF Institute at the
Quantum Computing in Finance Conference in
July 2022, about 47% said that their firms were
not currently considering Quantum Computing
technology at all. However, about 32% indicated
that their firms were researching use cases for
Quantum Computing technology, and another 21%
replied that their firms were actively investigating
Quantum Computing applications.
In a poll conducted by the CQF Institute at the
Quantum Computing in Finance Conference in
July 2022, the highest potential for Quantum
Computing was seen to be in Portfolio Optimization
(about 42%), followed by Risk Modeling (28%).
How is quantum computing used in
quantitative finance?
The use of quantum computing in quantitative finance is still in
its infancy. However, there are promising use cases. For example,
within portfolio optimization, quantum computing has been used
to process large numbers of assets and their correlations, providing
better risk-adjusted returns and identifying optimal asset allocations
in investment management. Within trading, it can analyze vast
amounts of data and identify hidden patterns, leading to the
development of more effective algorithmic trading strategies. For risk
management, quantum computing can simulate and analyze complex
financial instruments at high speed, which can help companies
better understand potential risks, such as credit risk, market risk, and
operational risk, enabling them to make better decisions.
These are just a few of the areas where quantum computing could
start to make a significant difference to the industry.
Skills for quantum computing
As interest in this emerging field continues to grow, it is important
for quants to ensure they have a competitive skillset that could put
them at the top of the job candidate list. Important skills for roles in
quantum computing include:
Quantum Mechanics: More specifically, the principles governing
quantum computing, such as superposition, entanglement, and
quantum gates, are essential concepts needed for professionals
considering entering this field.
Programming: Proficiency in languages like Python, is vital for writing
and implementing quantum algorithms.
Quantum Software Frameworks: Experience with quantum
computing platforms and libraries such as Qiskit (IBM) is important
as this can help quants facilitate the development and simulation of
quantum algorithms.
CQF CAREERS GUIDE | CAREER OPPORTUNITIES | 41
CAREER OPPORTUNITIES: GROWTH AREAS
Quantum Algorithms: Familiarity with algorithms, such as Grover’s,
Shor’s, and quantum machine learning algorithms are necessary
for quants to develop and apply quantum computing solutions to
financial problems.
Statistics: A strong knowledge of linear algebra, probability theory,
and statistics is crucial for quants to understand and manipulate
quantum states, as well as to model and analyze financial data.
Everything in quantum starts with education
and education comes in many forms and
many flavors. For an organization to achieve
a quantum mindset, everyone needs to speak
different dialects of quantum – engineering
dialects, the scientific dialects, business
dialects, but unless there is a common
foundation, it would be difficult to develop
solutions that create value for society. You
need to take responsibility for your education
and a good starting point is to take the courses
offered by the CQF, attend conferences, and
read as much as you can.
Esperanza Cuenca-Gómez, Head of Strategy and Outreach,
Multiverse Computing
As with machine learning and data science, continuous learning will
play a vital role for quants working in and on the periphery of this
field. As it is still just the beginning of the quantum era, quants will
need to watch developments in this space and ensure their skills stay
in line with industry requirements.
New career opportunities in quantum
computing
With the continued interest in this field, new career opportunities for
quants are beginning to emerge. These include:
Quantum Research Scientist: This role is at the intersection of
quantum computing and quantitative finance, researching new
applications and algorithmic advancements to solve complex
financial problems.
Quantum Software Engineer: Like a typical software engineer, these
quants design, implement and test software applications for the
financial industry; however they work with quantum programming
languages and platforms like Qiskit instead.
Quantum Financial Analyst: These quants develop and apply
quantum algorithms to financial models, evaluate and improve
investment strategies, and conduct risk analysis using quantum
computing techniques.
These roles and others can be found across the industry at companies
that are starting to invest in quantum technology in finance. As this
technology matures, the demand for the quants with the skills for
these roles is expected to grow, which will create even more job
opportunities in this exciting field.
Industry Insights
In a poll conducted by the CQF Institute at the
Quantum Computing in Finance Conference in
July 2022, most respondents believed that realtime deployment of Quantum Computing in
financial services is five to ten years away (49%).
About 28% believed that it is less that five years
away and another 23% believed that it is more
than ten years away.
CQF Corner
The CQF program contains lectures and an
advanced elective on quantum computing. There
are also several sources for further information
including books, conferences, and quantum
computing websites that provide access to
quantum computing programming information and
developer kits.
CQF CAREERS GUIDE | CAREER OPPORTUNITIES | 42
CAREER OPPORTUNITIES: GROWTH AREAS
PREPARING FOR THE FUTURE IN
QUANTITATIVE FINANCE
As fields like machine learning, data science, and quantum computing
continue to evolve, it is important for professionals in the industry to
sharpen the skills they need to stay competitive. Those who equip
themselves with advanced programming, analytical, and mathematical
skills will have more varied and interesting career paths to choose
from in the future.
This is where the value of continuous professional education and
development comes into play.
This year marks the 20th anniversary of the CQF program. Over
the past two decades, we have seen the demand for quant
professionals increase dramatically. Keeping abreast of developments
in the industry, the CQF curriculum has evolved in compelling
If I look around my office at the roles people
have in Bloomberg, I see that many are
complementing their current skills with
programming or data science courses. This is
an indication of how the industry had changed
and it underscores the demand for professional
certifications like the CQF.
Natalia Hencsey, Global Head of Sellside Risk Sales, Bloomberg
and relevant ways, drawing on the insights and experience of our
faculty, dedicated practitioners and academics with relevant industry
experience, and our alumni who continue to engage with the CQF
program through permanent access to the Lifelong Learning library
and the CQF Institute.
Two examples of CQF curriculum development include the addition
of Data Science and Machine Learning modules in 2017. The CQF
was one of the first professional quant finance qualifications to
advocate and teach these skills to delegates. These modules are
updated regularly to ensure they stay abreast with new developments
in the field. In 2021, quantum computing was added to the syllabus
as well, giving delegates an opportunity to engage with this emerging
professional field.
As the industry continues to adapt to new technologies and
techniques, the CQF faculty and staff will continue to enhance and
update the program content to ensure that all CQF delegates will
have a foundation in the most current skills and techniques sought
after by employers across the financial industry.
The CQF has always been about empowering
financial professionals to transform their careers.
Our focus on practical skills, combined with a
cutting-edge syllabus, has made the program
the preferred choice for professionals looking
to upskill. The CQF and CQF Institute will
always be at the forefront of financial education,
delivering the skills and knowledge that quants
need to succeed in the 21st century.
Dr. Randeep Gug, Managing Director, CQF and CQF Institute
CQF CAREERS GUIDE | CONCLUSION | 43
CONCLUSION
Opportunities for quants are on the rise, particularly in times of
turbulence for the financial markets. By taking time to develop
advanced skills in core quant domains, job candidates will
demonstrate their value to prospective employers.
Candidates should also bear in mind that they need to show
curiosity and interests in the field beyond just their university
studies, with practical projects and further education being
advised by recruiters.
For those looking to progress to more senior positions,
business skills including communication and collaboration
should also be nurtured and developed over time.
When preparing for the future in quantitative finance,
ongoing professional education is vital to ensure success in a
competitive environment. Professional designations, like the
globally recognized CQF program, provide a strong foundation
for building the essential skills needed to achieve your goals
throughout your career.
Download a brochure to find out more about how you could
achieve your career goals with the CQF.
CQF CAREERS GUIDE | ACKNOWLEDGMENTS | 44
ACKNOWLEDGMENTS
REFERENCES
ABOUT THE CQF
A number of people contributed to the development of The CQF
Careers Guide to Quantitative Finance 2023. First, we would like
to thank Richard Booty of Testwood Partners, Patrick Flanagan
of Clarence George, Dennis Grady of Spire Search Partners,
James Holland of Quant Capital, Lee Horan of Rec Finance, John
Meadowcroft of Anson McCade, and Tyler Robinson of Selby
Jennings, who shared insights on the quant job market from the
recruiter’s perspective.
CQF Institute resources include polling data from the Quant Insights
Conference hosted by the CQF Institute (November 2022), the
CQF Institute’s Quant Finance Careers Survey (December 2021),
and other polls conducted at CQF Institute events, including
Reinforcement Learning by Samit Ahlawat (April 2022), A Day in
the Life of a Portfolio Manager by Michael Althof (May 2022),
the Quantum Computing in Finance Conference (July 2022), the
Portfolio Management in Quant Finance Conference (March 2023),
and the panel discussion Quantitative Finance: Skills of the Future
(February 2023).
The Certificate in Quantitative Finance (CQF) is awarded by the
CQF Institute and delivered by Fitch Learning. The online program is
focused on teaching the essential skills used by quant practitioners
in today’s financial markets. The curriculum is updated quarterly
in consultation with faculty and senior alumni to ensure that the
skills taught in the program are meeting industry demand. Following
their graduation, all CQF alumni are given permanent access to
the CQF Lifelong Learning library to help them keep their skills
competitive throughout their careers. They also have access to
the alumni Career Services, which includes regular job posting
communications, CV advice, and more.
Thank you to the speakers at the Quant Insights Conferences and
panel discussion on Quantitative Finance: Skills of the Future for
sharing their industry insights. Most notably, we want to thank James
Jarvis of Trium Capital, Natalia Hencsey of Bloomberg, and Esperanza
Cuenca-Gómez of Multiverse Computing whose quotes have been
used in this Guide. Part of the research for The CQF Careers Guide
draws on polls that were conducted during some of our conferences
and talks throughout 2022 and 2023. The vibrant conversations,
on-site polling data, and thoughtful survey responses helped us gain
insight into your perspective on quant finance careers.
Finally, we gratefully acknowledge the many companies that have
supported the CQF program over the years. Ranging from investment
banks to hedge funds and recruiters, these firms have actively
participated in providing information on new job opportunities
through our newsletter. On behalf of the CQF program and our
alumni, we thank you very much for your contributions.
Salary table sources include Robert Half’s 2023 Salary Guide,
Robert Walters’ Salary Benchmarking Tool, and Selby Jennings The
Future of Quant: Global Market Report 2022. Additional resources
include eFinancial Careers, Glassdoor, Indeed, and the Argyll Scott
Technology and Data Analytics Top Skills Report 2022.
Where do Quants Work
CQF Institute’s Quant Insights Conference on Quantum Finance
A Day in the Life of a Lead Data Scientist
A Day in the Life of a Portfolio Manager
A Day in the Life of a Risk Manager
A Day in the Life of a Quant Advisor
A Day in the Life of a Quant Developer
A Day in the Life of a Quant Trader
For further information about the CQF program, visit www.cqf.com.
CERTIFICATE IN QUANTITATIVE FINANCE
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