Data Scientist, Equity Research

About the role

CFRA is seeking a Data Scientist to join its equity research technology team in India. The role blends quantitative finance with data science to create models and tools that support independent equity research and investment analytics.

Key responsibilities

• Design, build, and maintain quantitative models and machine‑learning pipelines for equity research, stock screening, and investment analytics.
• Partner with equity research analysts to translate valuation, financial‑statement analysis, earnings‑quality and sector frameworks into scalable data‑driven models.
• Source, clean, and engineer features from structured and unstructured financial data, including fundamentals, market data, earnings transcripts and alternative data sets.
• Develop and validate predictive models such as earnings forecasts, factor models and risk scores, and communicate results to technical and non‑technical stakeholders.
• Build and maintain data pipelines and automated workflows for model refresh and monitoring.
• Collaborate with software engineering teams to productionize models within CFRA’s research and analytics applications.
• Perform exploratory data analysis to identify new signals, themes or anomalies relevant to equity research.
• Document methodologies, assumptions and model limitations to institutional research standards.

Required profile

• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Financial Engineering, Economics or a related quantitative field.
• 3+ years of experience as a data scientist, quantitative analyst or similar role, preferably in financial services, asset management or equity research.
• CFA charter or active progress through the CFA program (Level II/III candidates strongly considered) with practical equity‑research experience.
• Strong understanding of financial statement analysis, valuation and investment methodology.

Required skills

• Python (pandas, NumPy, scikit‑learn; exposure to PyTorch/TensorFlow a plus).
• SQL and experience working with large datasets.
• Statistical modeling and machine‑learning techniques including regression, classification, time‑series analysis and factor/risk modeling.
• Natural Language Processing (NLP) for financial text.
• Data engineering and pipeline automation.