Quant Trader – Options & Systematic Derivatives – Banking/Financial Services Talent Corner HR Services

The Corporate Institute; Mumbai; Hybrid; INR 800,000 - 1,200,000; Full time

Job summary

Northstar in Mumbai is seeking a Quant Trader/Researcher to design, test, and trade systematic weekly options strategies on NSE and BSE. You will own ideas from hypothesis and backtest through to live deployment, with close book monitoring as trades run.

This hybrid role requires deep knowledge of options Greeks, volatility structures, and data science, plus Python—Pandas, NumPy, SciPy. You will work with trading and technology to scale research into real capital allocation.

Qualifications

• 3–5 years of experience in the options markets with hands-on trading.
• Bachelor’s/Master’s/PhD in quantitative fields.
• Strong knowledge of options pricing, Greeks, and risk.
• Familiarity with NSE/BSE weekly expiry and Indian market mechanics.
• Proficient in Python and data libraries; experience with large data sets.
• Ability to design, backtest, and deploy strategies end-to-end.

Responsibilities

• Design, backtest, and trade systematic weekly options strategies on NSE/BSE.
• Explore Greeks across weekly expiry to extract alpha.
• Research volatility structure and implied vs realized relationships.
• Use backtesting framework to test many strategy ideas.
• Engineer features from order flow and derivatives data.
• Apply ML for signal generation and risk estimation.
• Transition research into live deployment with trading and tech partners.

Skills

Python

Pandas

NumPy

SciPy

scikit-learn

Backtesting

Time series analysis

Quant finance

Risk metrics

Data analysis

Education

Bachelor's degree

Master's degree

PhD

Tools

Backtesting framework

Job description

Quant Trader / Researcher Options & Systematic Derivatives

About Northstar:

Northstar is a fully systematic, delta-neutral investment manager focused on India’s listed futures and options market, the world’s largest derivatives market by volume. Returns are harvested through coded rules, hard risk limits, and zero discretion.

We are building Northstar on a simple proposition: India’s family-office and UHNI capital should have access to a quantitative programme run to global institutional standards adapted for Indian markets, not imported. That means independent risk governance, a formal Investment Committee, and capacity discipline.

Backed by Crest Ventures, a publicly-listed group with three decades of building businesses across financial services and investments.

About the Role:

We are looking for a Quant Trader / Researcher to design, test, and trade systematic options strategies on the Indian markets with a primary focus on weekly index and stock options across NSE and BSE. This is a hybrid research-and-trading seat: you will own ideas end-to-end, from hypothesis and backtest through to live, capital-deployed execution, and stay close to the book as it trades.

Weekly options are unforgiving and information-rich – fast theta, sharp gamma, expiry-day dislocations, and volatility that reprices around events. We want someone who treats that as an edge, not a hazard, and who can build the research and risk machinery to trade it systematically.

Responsibilities:

• Design, backtest, and trade systematic weekly options strategies on NSE and BSE indices (Nifty, Bank Nifty, FinNifty, Sensex) and liquid single-stock options.
• Exploit the behavior of Greeks (delta, gamma, theta, vega) across the weekly expiry cycle as a source of alpha capturing theta decay, gamma dynamics, and expiry-day and pinning effects in strategy design.
• Research volatility structure implied vol surface, term structure, skew, IV crush around events and earnings, and the realized-vs-implied relationship.
• Use our existing backtesting framework to rigorously test, iterate on, and validate a high volume of distinct strategy ideas across timeframes and market conditions.
• Engineer features and signals from technical indicators, order flow, and derivatives data (open interest, PCR, OI shifts, basis) across intraday and daily timeframes.
• Apply machine learning and data-science techniques to signal generation, regime detection, and risk-reward estimation.
• Transition research into live deployment in partnership with trading and technology, then monitor live performance and adapt to changing market conditions.

Qualifications & Skills:

• 3 – 5 years of experience in the options markets, including hands-on options trading experience on a trading desk.
• Bachelor’s, Master’s, or PhD in a quantitative discipline Mathematics, Statistics, Engineering, Computer Science, Physics, or Quantitative Finance.
• Strong working knowledge of options and financial derivatives pricing, Greeks, and how they drive strategy construction and risk.
• Familiarity with Indian market structure (NSE/BSE), the weekly expiry calendar, and index/stock option mechanics.
• Solid grounding in data analysis: statistical modeling, time-series methods, and signal evaluation.
• Proficiency in Python and the core stack Pandas, Polars, NumPy, SciPy, scikit-learn; experience with backtesting or custom research tooling is a plus.
• Experience with large-scale historical and intraday data: cleaning, transformation, and feature extraction.
• Strong grasp of risk and performance metrics (Sharpe, Calmar, drawdown, exposure, hit-rate) and how they translate to live capital.
• Comfortable working independently in a fast-paced environment while collaborating with researchers, traders, and developers.
• Bonus: volatility modeling, options market-making or execution experience, ML applied to trading, or having built a backtesting framework from scratch.

Why Join Us:

• Build cutting-edge, research-driven derivatives strategies on some of the most liquid options markets in the world.
• Join a tight-knit, high-performing team where your ideas shape real positions and real P&L.
• Accelerate through hands-on exposure to live trading, deep research, and cross-disciplinary work with traders and technologists.
• Grow in an environment that rewards curiosity, experimentation, and long-term thinking over noise and shortcuts.