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 team

Data-Alternative team works across a large variety of structured and unstructured datasets — from time series to graphs — assessing predictive power and preparing them for modeling as needed.As data scientists, we work closely with the Research team to extract valuable insights from the data.

About the internship

Combining datasets from different sources is not trivial: they rarely share the same definitions or identifiers, vary in timing and detail, and often differing in quality and coverage.
The goal of the internship is to combine, aggregate, and embed these datasets to explore their mutual predictivity on companies' fundamentals and valuation.
The project will rely technically on Causal Discovery and Inference. The intern will review and apply traditional methods such as constraint-based (PC Algorithm, FCI, ..), score-based (GES, ..) and function-based (LinGAM) but also goes beyond and associates it with state-of-the-art machine learning techniques such as Double Machine Learning and Deep Learning.

6-months availability for the internship. 

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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Requirements

• Proficiency in Python, Pandas, Scikit Learn, Pytorch and Matplotlib. Spark is a plus.
• Experience in Time-series manipulation.
• Ability to aggregate and join heterogenous sources of data and to manipulate huge datasets.
• Capacity to propose new ideas, synthetize results, challenges, and proposals.