About the Role
BitQcode Capital runs fully automated systematic strategies across FX, commodities, global indices, and digital assets.
You will own research-to-production for a new or existing strategy book. This is not a sandbox role - models you build trade real capital within a defined risk envelope, and you are accountable for the live performance.
What you'll do
- Develop and validate systematic strategies across one or more asset classes
- Design alpha signals from price, order book, fundamental, and alternative data; defend them against multiple-testing and overfitting attacks
- Build the full research pipeline: data ingestion, feature engineering, backtesting, walk-forward validation, capacity and slippage modeling, live monitoring
- Own risk decomposition for your strategies - exposure, drawdown attribution, regime sensitivity, correlation to existing books
- Collaborate with the engineering side on production deployment (Python, MT5/MQL5, FIX/REST/WebSocket APIs); you do not need to write the production code yourself but you must be able to specify it precisely and read the code
- Contribute to firm-wide research infrastructure: factor libraries, backtesting frameworks, risk dashboards
Required
- PhD in a quantitative field (Statistics, Math, Physics) from IISc, TIFR, CMI, ISI or IITs (Applications from other institutions and disciplines will not be considered)
- Strong Python; comfort moving between research notebooks and production-grade code
- Demonstrable depth in: probability, statistics, time-series analysis, stochastic processes, derivatives pricing, market microstructure, financial instruments
- Statistical rigor - you should be the person who catches the lookahead bias, not the one who introduces it
- Ability to articulate why a strategy works in economic terms, not just statistical ones
- Experience - 1+ years in buy/sell side
