Founding Quant / Trading Strategist

Equity Only | Pre-Seed Startup | India Only

About Us

We are building a next-generation AI-driven trading platform(Ai-CoPilot) that ingests massive streams of financial, alternative, and sentiment data, transforming them into actionable insights. Our mission is to combine robust data engineering pipelines with Generative AI and advanced ML models to democratize access to sophisticated trading strategies.

You'll be the scientist who turns / tests our hypotheses / science into fundamental, risk-aware strategies.

Role Description

Own the alpha discovery → validation → portfolio construction → live monitoring loop. You'll design bias-resistant research, enforce model governance, and partner with AI / Backend to ship strategies that survive contact with the market (latency, costs, regime shifts). We expect hands-on Python and ruthless skepticism.

This role requires a DIY mindset with a willingness to educate the team about market strategies.

Qualifications

• Strong Market Risk and Analytical Skills, Experience in Quantitative Analytics
• Ability to work independently and remotely
• Relevant experience within the financial or trading industry is a plus
• Bachelor's degree in Finance, Mathematics, Statistics, Economics, or a related field
• 8+ years in quantitative research / trading or an equivalent track record of shipping strategies.
• Deep skills in Python (NumPy / Pandas / Numba / Statsmodels), time-series / stat inference, and ML for markets.
• Proven experience with bias-free backtesting , cross-validation for dependent data (e.g., CPCV ), and cost / impact modeling.
• Firm grasp of market microstructure and risk (exposures, drawdowns, regime shifts).
• Startup DNA : skeptical, fast, documentation-minded, and comfortable owning results end-to-end.

What You'll Do

First 90-120 Days (outcomes)

Why Join

Be the architect of the StonksAI, engine at day-zero, with meaningful equity and a direct line from your research to user outcomes

Skills Required

Numpy, Pandas, Python