pinpoint

Data Scientist, Equity Research

CFRA Research

Remote, IND

Full-time

Mid · 3+ years experience

Remote

Discovered 3 weeks ago

PythonpandasNumPyscikit-learnPyTorchTensorFlow

Free

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Key skills for this role

PythonpandasNumPy

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

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