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Medium: popular data scientist title, mid-level (3+ years) and metro hiring, but finance specialization narrows candidates.
High: requires equity research, valuation skills, and CFA progression, limiting cross-industry transferability.
High: explicit 3+ years requirement, finance domain experience, and mandatory data science tech stack and CFA preference.
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Design, build, and maintain quantitative and machine learning models supporting equity research and investment analytics.
Collaborate closely with equity research analysts to translate financial methodologies into scalable data-driven models and signals.
Develop, validate, and productionize predictive models and automated data pipelines for continuous research and analytics platform use.
Bachelor's or Master's degree in a quantitative field (Data Science, Statistics, Computer Science, Financial Engineering, Economics, or related).
3+ years experience as a data scientist or quantitative analyst, ideally in financial services, asset management, or equity research.
Strong proficiency in Python (pandas, NumPy, scikit-learn) and SQL; familiarity with machine learning and statistical techniques.
CFA charter or active progress (preferably Level II/III) with practical equity research, valuation, or investment analysis experience.
Experience operating at the intersection of quantitative finance and data science with strong domain expertise in equity research.
Proven ability to work cross-functionally with research analysts to convert complex financial concepts into algorithmic models.
Background includes handling large financial datasets, financial statement analysis, valuation methods, and potentially NLP on financial texts.