Quantitative Developer Intern

About Skylife Research

Skylife Research is a research and technology firm focused on Indian financial markets. We combine quantitative research, market data, and software development to build analytical tools and develop systematic trading strategies. Our work includes stock correlation and network analysis, options analytics, and strategy development across Indian equities and derivatives.

About the Role

We are looking for a Quantitative Developer Intern to help research, build, and test systematic trading strategies, with a particular focus on Indian index and stock options. You will work on the full research process—from collecting and analysing market data to developing trading rules, backtesting strategies, and monitoring them through paper trading. You will also contribute to our Correlation Graph and explore how market relationships and changing volatility conditions can inform trading decisions.

Key Responsibilities

• Develop options strategies: Research directional, volatility-based, and hedged strategies. Define systematic rules for entries, exits, strike selection, position sizing, and adjustments.
• Build options analytics: Analyse implied versus realised volatility, volatility skew, term structure, open interest, and Greeks to evaluate strategies and their exposures.
• Create backtesting frameworks: Test strategies using historical data, accounting for transaction costs, slippage, bid–ask spreads, liquidity, margin requirements, and expiry mechanics.
• Validate research: Use out-of-sample testing, walk-forward analysis, and sensitivity checks to assess whether results hold across different market conditions.
• Develop data pipelines: Collect, clean, and process equity and options data through broker APIs, historical datasets, and live feeds.
• Build risk tools: Track portfolio Greeks, capital utilisation, drawdowns, and concentration. Evaluate strategy behaviour during large price moves and volatility shocks.
• Contribute to correlation research: Explore how stock correlations, network structures, and market regimes can support strategy selection and portfolio construction.
• Support paper trading: Implement research ideas in paper-trading workflows, monitor results, and investigate differences between backtested and observed performance.
• Write maintainable code: Build modular Python tools and document assumptions, methodology, results, and limitations.

Requirements & Tech Stack

• Strong Python programming skills, including pandas and NumPy.
• A solid foundation in probability, statistics, and linear algebra.
• Experience analysing time-series data and working with imperfect datasets.
• Basic understanding of options, payoff structures, implied volatility, and Greeks.
• Familiarity with backtesting concepts and research pitfalls such as overfitting and look-ahead bias.
• Working knowledge of Git and an ability to write clear, structured code.
• Strong problem-solving skills and the ability to explain technical findings clearly.

Good to Have

• Personal or academic projects involving options, quantitative research, or systematic trading.
• Experience with SQL/PostgreSQL, broker APIs, WebSockets, FastAPI, or Redis.
• Familiarity with Indian equity and derivatives markets.
• Exposure to graph theory, correlation modelling, optimisation, or machine learning.

Qualifications

Students or recent graduates in Computer Science, Engineering, Mathematics, Statistics, Economics, Finance, or related fields. We value practical projects, curiosity, and sound reasoning over a specific degree or prior finance internship.

Location & Commitment

Full-time internship based in Mumbai, with a hybrid working arrangement and some flexibility to work from home.

How to Apply

• Share your CV and relevant GitHub repositories, research notebooks, or projects. If you have tested a trading strategy, include a brief explanation of the idea, how you tested it, and what you learned.