POSITION OVERVIEW

We are seeking a Senior Leader - Data Science to lead our quantitative research and AI initiatives. This is a strategic leadership role based in Pune, designed for a professional who can blend deep financial market intuition with advanced Generative AI and quantitative modeling.

INITIAL 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.

EDUCATION AND EXPERIENCE

- 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.

Key Performance Indicators (KPIs)

- 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.