Quant Algo Strategist - Algorithmic Trading - Banking/Financial Services
HR LOBBY · Gurugram · onsite · Posted 8d ago
About a minute. You'll make an account on the way through.
About this role
About the Role:
Company is hiring one exceptional Quant Strategist to build and lead the strategy engine behind Algo Wealth - our flagship automated trading product.
You will design, backtest, and own a portfolio of high-performing, risk-controlled algorithmic strategies across Nifty, Bank Nifty, Sensex, Stock F&O, and Commodities. Your strategies will be packaged into defined Capital Tiers (Rs 1.5 - 5 Lakh up to Rs 50 Lakh+) for retail and HNI investors.
We judge strategies on risk-adjusted consistency and capital preservation, not headline returns.
What You Will Do:
Strategy Engineering:
- Design and backtest options, futures, and hedged combinations across various market regimes.
Capital-Tier Architecture:
- Build strategy books tailored to specific capital ranges (Rs 1.5 - 5L, 5 - 15L, 15 - 50L, 50L+) with tier-specific risk and diversification logic.
Risk & Validation:
- Set stop-loss logic, position sizing, max drawdown limits (8 - 12%), kill-switch rules, and run out-of-sample/walk-forward tests.
Live Management:
- Monitor live execution, control slippage, track strategy decay, and retune or retire strategies on a defined schedule.
Product & Compliance:
- Work with Engineering on order execution, and with Product/Compliance to create clear strategy factsheets for users.
Must-Have Qualifications:
Verified Live Track Record:
- Mandatory 1+ continuous year of live trading P&L in Indian F&O (backed by Sensibull verified P&L, broker contract notes, or audited statements). Backtest-only or screenshot claims will be rejected.
Metrics Standard (Net of Costs/Slippage):
- Ability to demonstrate a Sharpe Ratio >= 1.5, Sortino >= 2.0, Win Days >= 60%, and Profit Factor >= 1.6 on live or out-of-sample performance.
Domain & Tech Mastery:
- Deep knowledge of options Greeks, IV, market microstructure, SPAN margin rules, and 50/50 cash/non-cash collateral split rules. Hands-on experience with Python (pandas, NumPy) and tick/minute data.
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