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.
Responsibilities
- 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.
Nice to Have
- 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