Capital Fund Management (CFM)

www.cfm.com

2 Jobs

409 Employees

About the Company

Capital Fund Management (CFM) is a successful alternative investment manager and a pioneer in the field of quantitative trading applied to capital markets across the globe. Our methodology relies on statistically robust analysis of terabytes of financial data for asset allocation, trading decisions and automated order execution.

CFM is an appealing career destination for highly-talented and passionate PhDs, IT engineers and experts from around the world. Our people can rely on original theoretical insight accumulated over 30 years of market experience, as well as cutting-edge technology and disciplined approaches.

These fundamentals allow us to foster the creation of exciting opportunities and state-of-the-art trading strategies. Our people's diversity and dedication contribute to CFM's unique culture of research, innovation and achievement.

The company is regulated by AMF, the SEC and the CFTC, with assets under management of $13.8 billion (Feb 2024).

Listed Jobs

Company Name: Capital Fund Management (CFM)
Job Title: Predictive Modeling with Alternative Data - Internship
Job Description: Job Title: Predictive Modeling with Alternative Data Intern Role Summary: Intern will develop predictive models that combine heterogeneous structured and unstructured datasets to assess their impact on company fundamentals and valuation. The role combines causal discovery techniques with advanced machine learning (double machine learning, deep learning) to uncover predictive relationships across multi‑source data. Expectations: - Complete a 6‑month project focusing on data integration, causal inference, and predictive modeling. - Deliver actionable insights and prototype models for the research team. - Communicate findings clearly to technical and non‑technical stakeholders. Key Responsibilities: - Acquire, cleanse, and merge datasets from varied sources, ensuring consistency of identifiers and time alignment. - Engineer features—including time‑series transformations and graph‑based representations—suitable for downstream modeling. - Implement traditional causal discovery algorithms (PC, FCI, GES, LinGAM) and evaluate their assumptions and outputs. - Extend causal inference with Double Machine Learning frameworks and deep learning architectures to improve prediction accuracy. - Conduct rigorous model validation, sensitivity analyses, and robustness checks. - Produce visualizations and concise reports summarizing model performance and key drivers. - Propose methodological enhancements and novel modeling strategies. Required Skills: - Proficient in Python (Pandas, Scikit‑learn, PyTorch, Matplotlib). - Experience with Spark or similar distributed data processing tools (plus). - Strong knowledge of time‑series manipulation and analysis. - Ability to handle and join large, heterogeneous datasets. - Familiarity with causal inference concepts and algorithms. - Solid programming habits, version control (Git), and unit testing. - Excellent analytical thinking, problem‑solving, and written/verbally communication. Required Education & Certifications: - Bachelor’s (or higher) degree in Computer Science, Statistics, Data Science, Applied Mathematics, Finance, Economics, or related quantitative field. - Coursework or coursework certification in machine learning, causal inference, or deep learning is preferred.

Paris, France

On site

11-12-2025

Company Name: Capital Fund Management (CFM)
Job Title: Data Scientist - Macroeconomic data set - 3 years XP minimum
Job Description: Job Title: Data Scientist - Macroeconomic Data Role Summary: Transform macroeconomic and alternative datasets into actionable insights for quantitative strategies through statistical analysis, machine learning (ML) model design, and production pipeline development. Focus on alpha generation by identifying predictive patterns and supporting quantitative research. Expectations: 0–3 years of hands-on data manipulation in production environments. Master’s degree in machine learning, data science, or equivalent. Key Responsibilities: Analyse data structures and patterns; extract predictive features; design ML models for forecasting; identify new modelling pathways for business challenges; collaborate with quantitative researchers; manage technical relationships with data providers; develop scalable, monitored production pipelines; contribute to team knowledge sharing. Required Skills: Proficiency in Python (numpy, pandas, polars, scikit-learn); expertise in time series analysis and ML forecasting; familiarity with macroeconomic data; cloud computing experience (e.g., AWS); strong analytical, autonomous problem-solving, and communication skills. Required Education & Certifications: Master’s degree in machine learning, data science, or related quantitative field; no certifications specified.