Data Scientist, Equity Research

This is a fully remote position, open to applicants in India.

📋 Description

• Create, develop, and sustain quantitative models and machine learning pipelines that facilitate equity research, stock screening, and investment analytics.

• Collaborate with equity research analysts to convert valuation methods, financial statement evaluations, earnings quality assessments, and sector-specific approaches into scalable, data-driven models.

• Extract, cleanse, and engineer features from both structured and unstructured financial data, encompassing fundamentals, market information, earnings transcripts, and alternative datasets.

• Construct and validate predictive models such as earnings forecasts, factor models, and risk scoring models.

• Present model outcomes to both technical and non-technical audiences.

• Develop and oversee data pipelines and automated workflows for model updates and monitoring.

• Work alongside software engineering teams to implement models in CFRA's research and analytics platforms.

• Conduct exploratory data analysis to uncover signals, themes, and anomalies pertinent to equity research.

• Document methodologies, assumptions, and model limitations in accordance with institutional research standards.

• Keep abreast of developments in quantitative finance, NLP for financial text, and machine learning methodologies relevant to investment research.

⛳️ Requirements

• Bachelor’s or Master’s degree in a quantitative discipline such as Data Science, Statistics, Computer Science, Financial Engineering, Economics, or a related field.

• Over 3 years of experience as a data scientist, quantitative analyst, or in a similar capacity, preferably within financial services, asset management, or equity research.

• CFA charter or active progression through the CFA Program (Level II/III candidates are highly encouraged), with practical experience in equity research, valuation, or investment analysis.

• Strong expertise in Python for data science (including pandas, NumPy, scikit-learn; familiarity with PyTorch/TensorFlow is a plus).

• Solid foundation in statistics and machine learning techniques: regression, classification, time-series analysis, and factor/risk modeling.

• Proficient in SQL and adept at working with extensive 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).

• Familiarity with NLP techniques applied to financial texts (such as earnings call transcripts, filings, and news) is advantageous.

• Knowledge of cloud platforms (AWS preferred) and version control systems (Git).

• Exceptional analytical, written, and verbal communication skills, with the capability to convey complex quantitative concepts to research and business stakeholders.

• Strong attention to detail and a methodical, hypothesis-driven approach to analysis.

• Ability to handle multiple projects and meet deadlines in a dynamic research environment.

• Prior experience at a sell-side or buy-side research firm, credit rating agency, or independent research provider.

• Exposure to alternative data sources (such as satellite, web-scraped, and transaction data) for investment research.

• Familiarity with backtesting frameworks and portfolio construction principles.

🏝️ Benefits

• 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