Quantitative researcher

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.

POSITION

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 experimental or theoretical science (mathematics, physics, statistics, economics, life science etc.) or in a computational scientific field (big data, computer science, computational biology or chemistry, engineering)
• 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.
 

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