Job Title

Singularity · Quantitative Trading · Full-time Paid InternshipABOUT THE ROLEAlpha begins with features. This role sits at the intersection of market microstructure and machine learning — your job is to translate raw market data into structured, signal-rich features that trading models can learn from. You will work across the firm's data stack, strategy research, and ML pipeline to build and validate the feature library that powers Singularity's quantitative edge in NIFTY 50 options and Indian derivatives.This is not a pure engineering role. You need to understand why a feature might carry information — and be rigorous about proving that it does.WHAT YOU WILL DODesign and construct features across three domains: technical indicators, market microstructure (order flow imbalance, bid-ask dynamics, volume profiles), and regime detection signalsBuild options-specific features — IV surface shape, skew, term structure, realized vs. implied vol spreads, open interest shiftsDevelop and maintain the firm's canonical feature library with versioning, documentation, and out-of-sample validationRun feature validation end-to-end: information coefficient (IC), decay analysis, correlation, and combinatorial purged cross-validationCollaborate with the research environment — implement and test strategies in notebooks, validate feature behaviour against backtest resultsWork with the AI research team to ensure features are correctly specified for model ingestion across the intelligence layerIdentify and flag overfitting patterns, data leakage, and look-ahead bias throughout the feature pipelineREQUIREDCurrently pursuing or recently completed a degree from A Tier 1 instituteIf above is not true, then a minimum of 1 year of relevant work experienceStrong Python — pandas, NumPy, scikit-learn; comfortable with large time-series datasetsDemonstrated understanding of financial ML — not just ML applied to finance, but the specific pitfalls: leakage, non-stationarity, low signal-to-noiseFamiliarity with Lopez de Prado's framework or an equivalent rigorous approach to financial feature constructionKnowledge of derivatives market mechanics — how options price, vol surfaces, GreeksStatistical discipline: hypothesis testing, IC analysis, walk-forward validationGOOD TO HAVEHands-on experience with NSE derivatives dataExperience building factor models or signal research pipelinesFamiliarity with alternative data — FII/DII flows, options positioning dataResearch background — ability to read and implement academic papersUnderstanding of event-driven features: rollover dynamics, expiry effects