Data Scientist

Paris, 75, FR

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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.

Your Role

As a Senior Data Scientist in the Data Corporate team, you will turn fundamental company data into high-impact, research-ready features and signals that feed the alpha models designed and owned by our Researchers.

Working side by side with them, as well as with data engineers and external providers, you will take full ownership on moving from raw and legacy to robust, point-in-time datasets and then explore those datasets, proposing and validating new features, signals, and statistical or machine-learning approaches.

This is a data-science-first, senior individual-contributor role for a top-tier practitioner with deep fundamental-data expertise and sharp analytical judgement, directly accountable for data quality, research usability, and measurable alpha contribution, within an ambitious, intellectually demanding environment that remains supportive, innovative, and collaborative.

Key Responsibilities

Act as the team's subject-matter expert on fundamental company data (financial statements, estimates, corporate actions, ownership, reference data), mastering vendor schemas, accounting conventions, restatements, and temporal characteristics

Own technical and data-quality relationships with providers, from evaluation and onboarding to escalation, roadmap discussions, and service reviews
Evaluate new data sources, methodologies, and technologies, separating durable research value from vendor claims and short-lived trends

Feature & Signal Research

Design economically grounded features from fundamental data and assess their stability, coverage, turnover, and behavior across markets and regimes before they enter alpha research

Develop and critically evaluate statistical and machine-learning approaches, prioritizing economic intuition, interpretability, point-in-time integrity, and genuine out-of-sample robustness

Proactively identify new use cases by connecting accounting, corporate, and market-structure knowledge to systematic investment questions, and test whether they deliver differentiated alpha

Research Partnership & Team Leadership

Work side by side with Researchers to understand their needs, while independently proposing datasets, features, signals, diagnostics, and reproducible workflows that open new research directions (alpha model ownership remains with Quantitative Researchers)

Raise the bar for the team through design reviews, documentation, mentoring, and active knowledge sharing across research and engineering

Data Quality & Platform Stewardship

Take ownership of the Data Corporate Platform and its curated-data pipelines, rationalizing legacy implementations and improving documentation, lineage, discoverability, and fitness for quantitative research

Define and enforce standards for point-in-time correctness, survivorship-bias control, revisions, outlier treatment, completeness, and cross-provider reconciliation

Maintain and enhance production pipelines (testing, monitoring, incident resolution, backfills, schema changes), with data engineering support where relevant

Your Skills

Education & Experience

Master's degree or PhD in a quantitative discipline (mathematics, statistics, computer science, engineering, econometrics, or finance)

10+ years of experience in quantitative finance, systematic investing, financial data, or a closely related production environment, with a proven record among top-tier practitioners (at least 5+ years as a Data Scientist)

Proven, hands-on expertise in fundamental data within a quantitative investment context: point-in-time datasets, financial statements, estimates, corporate actions, symbology, entity mapping, restatements, and bias controls

Demonstrated ability to turn complex financial data into impactful research outputs, from exploratory analysis and economic framing to feature engineering, signal development, model evaluation, and production deployment

Track record of independently proposing new data-science approaches anddelivering high-impact features, signals, or models, while upholding rigorous standards for data quality, reproducibility, and