Data Analyst

Data Analyst We are looking for a Data Analyst to join a public equities investment team pursuing a quantamental strategy across alternative data and fundamental research. The role is centered on sourcing, validating, and scaling differentiated datasets that can improve security selection and deepen our understanding of company-specific and industry-level inflections. You should be equally comfortable working with messy data, testing hypotheses rigorously, and framing outputs in an investment context.

Build and maintain workflows to ingest, clean, map, and analyze large-scale structured and unstructured datasets Evaluate alternative data for relevance, integrity, coverage, timeliness, and predictive power Conduct signal testing and backtesting to determine whether a dataset adds value to the investment process Work closely with the investment team to turn raw data into actionable views on revenue trends, KPIs, competitive dynamics, and inflection points Use AI agents and automation to accelerate research, tool-building, and internal processes Support basic 3-statement modeling and connect data findings to fundamental underwriting Requirements 1–3 years of experience, ideally at an alternative data vendor in research, product, or analytics Strong Python and SQL skills Demonstrated interest in public markets investing and differentiated research Ability to distinguish signal from noise and apply judgment, not just run analysis High ownership, speed, and intellectual curiosity Background The most direct fit is someone with 1–3 years at an alternative data provider, but we are open to non-traditional candidates. Strong candidates may also come from backgrounds such as investment banking with self-taught coding, or legal, market research, or other analytical roles where they independently built scrapers, worked with data, and developed a genuine investing process. We care more about investor instinct, analytical horsepower, and resourcefulness than a perfectly standard resume.

Experience with large-scale data analysis across very large datasets Web scraping, entity resolution, or API-based data collection Familiarity with signal evaluation, time-series analysis, or statistical testing Understanding of company KPIs, operating metrics, and financial statements Personal investing experience or clear evidence of market obsession