Capital Fund Management

Position Title/Short Description

Full Text of JOE Listing:

About CFM

Founded in 1991, we are a global quantitative and systematic asset management firm applying a scientific approach to finance to develop alternative investment strategies that create value for our clients.
We value innovation, dedication, collaboration, and the ability to make an impact. Together, we create a stimulating environment for talented and passionate experts in research, technology, and business to explore new ideas and challenge existing assumptions.


About The Role
As a Quantitative Researcher, you will extract insights from a wide range of datasets – from traditional financial data to unconventional alternative data – to design and develop sophisticated systematic trading models. Your work will focus on building new strategies and enhancing those already implemented at CFM.

You will join a team of 100+ researchers and collaborate closely with data engineers and software engineers to turn research ideas into robust, production-ready trading signals.

What you will do :

-Push the research frontier
Investigate and apply cutting-edge statistical, econometric, and machine‑learning techniques.
Continuously refine models and methods based on empirical evidence.

-Generate and test investment ideas
Formulate new investment hypotheses and research questions.
Design and execute rigorous statistical analyses and backtests to validate (or invalidate) these ideas.

-Explore and assess data
Evaluate datasets (traditional and alternative) for their informational content and usability.
Transform promising data into strategies used in our research and production environment.

-Build and deploy trading signals
Transform research results into robust, additive trading insights.
Work with engineering teams to implement and maintain these signals in production.

The ideal candidate combines strong creativity, to devise novel ways of uncovering hidden statistical patterns, with a high degree of rigor in testing and validating ideas. Experience working with complex datasets using machine-learning and/or econometric techniques is highly valued. While a strong interest in finance is essential, prior professional experience in the field is not required.


Ideal Candidate
PhD in economics/macroeconomics - topics of research linked with bonds, economic indexes, inflation, central banks intervention etc
Post PhD experience (academic or private sector research)
Understanding of the ins and outs of machine learning algorithms—and can tweak them as needed
Experience with applied machine learning / econometrics to large datasets
Programming skills in Python
Adaptable and rigorous, capable of working in a quickly evolving environment
Strong teamwork and communication skills


Equal Opportunities Statement

We are continuously striving to be an equal opportunity employer and we prohibit any discrimination based on sex, disability, origin, sexual orientation, gender identity, age, race, or religion. We believe that our diversity, breadth of experience, and multiple points of view are among the leading factors in our success.
CFM is a signatory of the Women Empowerment Principles.


Follow Us

Follow us on Twitter or LinkedIn or visit our website to find out more about CFM.