Data Scientist, Equity Research
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Summary
Build quantitative models and ML pipelines to power equity research, turning financial data into research-grade signals and insights for analysts and clients.
CFRA is looking for a Data Scientist to join our equity research technology team in India. This role sits at the intersection of quantitative finance and data science, applying statistical modeling, machine learning, and financial domain expertise to build tools that power CFRA's independent equity research and investment analytics platforms.
The ideal candidate combines strong data science and programming skills with a genuine grounding in financial markets and equity analysis — someone who can translate raw financial data into research-grade signals, models, and insights that analysts and clients rely on. A CFA charter (or progress toward one) and hands-on equity research experience are strongly preferred, as this role requires close collaboration with research analysts and a working understanding of financial statement analysis, valuation, and investment methodology.
- Design, build, and maintain quantitative models and machine learning pipelines that support equity research, stock screening, and investment analytics
- Partner with equity research analysts to translate research methodologies (valuation, financial statement analysis, earnings quality, sector-specific 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 (e.g., earnings forecasts, factor models, risk scoring) and communicate results to both technical and non-technical stakeholders
- Build and maintain data pipelines and automated workflows for ongoing 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
- Stay current on developments in quantitative finance, NLP for financial text, and machine learning techniques applicable to investment research
- Bachelor's or Master's degree in a quantitative field such as Data Science, Statistics, Computer Science, Financial Engineering, Economics, or related discipline
- 3+ years of experience as a data scientist, quantitative analyst, or similar role, ideally within financial services, asset management, or equity research
- CFA charter, or active progress through the CFA Program (Level II/III candidates strongly considered), with practical experience in equity research, valuation, or investment analysis
- Strong proficiency in Python for data science (pandas, NumPy, scikit-learn; exposure to PyTorch/TensorFlow a plus)
- Solid grounding in statistics and machine learning techniques: regression, classification, time-series analysis, and factor/risk modeling
- Proficient in SQL and working with large financial datasets from relational databases and data warehouses
- Experience with financial statement analysis, equity valuation methods (DCF, comparables, precedent transactions), and market data sources (e.g., Capital IQ, FactSet, Bloomberg)
- Experience with NLP techniques applied to financial text (earnings call transcripts, filings, news) is a plus
- Familiarity with cloud platforms (AWS preferred) and version control (Git)
- Excellent analytical, written, and verbal communication skills, with the ability to explain complex quantitative concepts to research and business stakeholders
- Strong attention to detail and a rigorous, hypothesis-driven approach to analysis
- Ability to manage multiple projects and deadlines in a fast-paced research environment
Preferred Qualifications
- Prior experience at a sell-side or buy-side research firm, credit rating agency, or independent research provider
- Exposure to alternative data sources (satellite, web-scraped, transaction data) for investment research
- Familiarity with backtesting frameworks and portfolio construction concepts
- 21 days of Vacation
- 8 Sick Days
- 1 paid volunteer day
- 11 - 13 Holidays a year
- Health Insurance
- Company paid Life & Disability Insurance
- Competitive Pay
- Annual Performance Bonus