Product Manager

About Company:

Bullsmart is a pioneering technology-driven brokerage firm on a mission to democratize sophisticated trading tools for the Indian retail investor. We are building a next-generation trading platform that empowers our users with institutional-grade analytics, seamless execution, and intelligent strategy-building capabilities. As we are in the initial phase of our product development, you will have a unique opportunity to shape the core DNA of our offerings and make a tangible impact from the ground up.

The Role: Where Quant Meets AI

We are seeking a Product Manager with a strong Financial Engineering background to be the architect of our strategy intelligence. Your primary mission will be to design and build a next-generation strategy ecosystem that harnesses AI to democratize quantitative trading. You will transform sophisticated financial models into intuitive, adaptive, and highly personalized strategy recommendations for stocks, options, and futures.

You will be the bridge between our quant researchers, AI engineers, and the end-user, ensuring that our strategy products are not only powerful but also profoundly user-centric.

Key Responsibilities

• Strategy Product Vision: Define and own the product roadmap for our core strategy offerings, including AI-driven strategy recommendation engines, portfolio optimizers, and backtesting modules.
• Quant-to-Product Translation: Collaborate with quant analysts and data scientists to productize complex strategies (e.g., Volatility Arbitrage, Delta-Neutral Hedging, Pairs Trading) into scalable, explainable features for retail investors.
• AI-Personalization: Lead the development of user risk profiling and preference systems that allow our AI to tailor strategy recommendations (e.g., "Iron Condor for steady income", "Long Call for high conviction") to individual risk appetites and market views.
• End-to-End Execution: Manage the entire product lifecycle from concept to launch, creating detailed PRDs, prioritizing the backlog, and working closely with full-stack and AI/ML engineers to build, test, and iterate rapidly.
• Data-Driven Validation: Establish a framework for A/B testing and performance tracking of recommended strategies to create a closed-loop learning system for our AI models.
• Stakeholder Communication: Utilize exceptional written and verbal English skills to articulate complex concepts clearly to diverse teams, including engineers, executives, and compliance, ensuring alignment and efficient execution.

What You Will Build

• Strategy Marketplace/Ecosystem: A platform where users can discover, customize, and deploy pre-built quant-and-AI-validated strategies for different market regimes.
• Intelligent Strategy Recommender: An AI engine that analyzes a user's portfolio, risk tolerance, and market outlook to suggest optimal trades and strategies.
• Interactive Backtesting Tool: A user-friendly interface that allows users to simulate strategy performance against historical data, with key metrics like Max Drawdown, Sharpe Ratio, and P&L.
• Portfolio-Level Risk Analytics: Features that help users understand the aggregate risk (Delta, Gamma, Vega) of their multi-leg F&O positions and equity holdings.

Ideal Profile (Must-Haves)

• A bachelor's or master's degree in Financial Engineering, Quantitative Finance, Computational Finance, or a similarly rigorous quantitative field.
• 3-5 years of experience in product management within a trading, fintech, or quant-driven environment, with a proven track record of shipping products related to trading strategies, analytics, or algorithmic execution.
• Deep understanding of derivative pricing models, Greek sensitivities, and popular Volatility & Options trading strategies. Experience with backtesting methodologies is crucial.
• Strong technical proficiency to collaborate effectively with AI/ML and data engineering teams. Familiarity with Python (Pandas, NumPy) and basic SQL is highly desirable to understand data structures and model requirements.
• AI/ML Curiosity: A solid understanding of how machine learning (e.g., reinforcement learning for strategy allocation, NLP for news sentiment integration) can be applied to financial markets. You don't need to build the models, but you must speak the language.
• Exceptional English proficiency: Excellent written and verbal communication skills in English are essential for creating clear documentation and leading cross-functional team discussions.