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About the role
As a Senior Leader - Data Science at our company, your role is to lead quantitative research and AI initiatives. You will be based in Pune and expected to merge profound financial market intuition with advanced Generative AI and quantitative modeling. Key Responsibilities: - Strategic Roadmap: Define and execute a long-term plan to integrate Data Science into the core of the equity research process. - GenAI & LLM Strategy: Lead the development of proprietary LLM frameworks to synthesize vast amounts of financial data and earnings transcripts into actionable investment signals. - Framework Institutionalization: Translate the qualitative expertise of veteran fund managers into systematic, repeatable quantitative models. - Team Leadership: Recruit, mentor, and lead a high-performing team of Quants and Data Scientists. Quantitative & Market Research: - Model Development: Design, develop, and backtest quantitative models to identify asymmetric return opportunities. - Factor Research: Conduct rigorous factor research across valuation, quality, and momentum, ensuring all models maintain economic interpretability. - Risk Analytics: Implement stress testing, Monte Carlo simulations, and predictive risk analytics to ensure portfolio resilience across various market regimes. - Performance Attribution: Produce data-backed attribution reports (Sharpe, Sortino, Treynor) and communicate findings to the Investment Committee. Qualifications: - Education: A PhD or Master's degree in Statistics, Mathematics, Physics, or Financial Engineering. - Experience: 5-8 years of professional experience in Data Science or Quantitative Research, with a strict focus on Financial Markets (Asset Management, Wealth Management, or Buy-side Research). - Domain Expertise: Strong understanding of Indian equity markets and the fundamental drivers of long-term wealth creation. Technical Skills & Competencies: 1. Advanced Statistical Modeling - Systematic Alpha: Proven ability to build models for stock selection and portfolio construction. - Predictive Analytics: Deep experience in applying Machine Learning (Random Forest, Gradient Boosting, Deep Learning) to financial datasets while mitigating look-ahead and survivorship biases. - Time Series: Mastery of ARIMA, GARCH, and Bayesian inference for market regime detection. 2. Generative AI & LLM Integration - RAG (Retrieval-Augmented Generation): Designing systems that allow the research team to "query" years of annual reports and internal research notes. - Investment Synthesis: Leveraging LLMs to automate the extraction of qualitative "moats," management quality indicators, and risk factors. - Agentic Workflows: Developing AI agents to automate complex research tasks and cross-reference diverse data streams. In this role, you will be measured by Key Performance Indicators (KPIs) such as: - Systematization: Successful conversion of manual research processes into data-backed, backtested frameworks. - Alpha Generation: Measurable contribution of quantitative signals to identifying high-growth equity opportunities. - Efficiency: Reduction in time-to-insight for the research team through the deployment of LLM-based tools. As a Senior Leader - Data Science at our company, your role is to lead quantitative research and AI initiatives. You will be based in Pune and expected to merge profound financial market intuition with advanced Generative AI and quantitative modeling. Key Responsibilities: - Strategic Roadmap: Define and execute a long-term plan to integrate Data Science into the core of the equity research process. - GenAI & LLM Strategy: Lead the development of proprietary LLM frameworks to synthesize vast amounts of financial data and earnings transcripts into actionable investment signals. - Framework Institutionalization: Translate the qualitative expertise of veteran fund managers into systematic, repeatable quantitative models. - Team Leadership: Recruit, mentor, and lead a high-performing team of Quants and Data Scientists. Quantitative & Market Research: - Model Development: Design, develop, and backtest quantitative models to identify asymmetric return opportunities. - Factor Research: Conduct rigorous factor research across valuation, quality, and momentum, ensuring all models maintain economic interpretability. - Risk Analytics: Implement stress testing, Monte Carlo simulations, and predictive risk analytics to ensure portfolio resilience across various market regimes. - Performance Attribution: Produce data-backed attribution reports (Sharpe, Sortino, Treynor) and communicate findings to the Investment Committee. Qualifications: - Education: A PhD or Master's degree in Statistics, Mathematics, Physics, or Financial Engineering. - Experience: 5-8 years of professional experience in Data Science or Quantitative Research, with a strict fo