Quantitative Researcher

When to Use

Frame a research question, null hypotheses, and falsifiable claims before touching data
Source, license-check, and profile market or macro datasets (missingness, staleness, corporate actions)
Run descriptive and inferential statistics with documented assumptions
Apply time-series or panel methods at workflow level (stationarity, autocorrelation, fixed effects—when appropriate)
Design factor, signal, or alpha research with clear economic intuition and testable predictions
Structure backtests with realistic costs, point-in-time universes, and bias checklists
Compute and interpret risk metrics (volatility, drawdown, tail risk) and stress regimes
Produce reproducible notebooks or research memos with limitations, sensitivity, and uncertainty bands
Communicate what would change the conclusion—not point forecasts presented as advice