Key Responsibilities:

Framework Development & Implementation:

- Design and implement a comprehensive framework for identifying multi-bagger stocks across market capitalisations.

- Develop systematic processes to analyse market trends, sector performance, and individual stock potential.

- Create and maintain models for scenario analysis incorporating various macroeconomic factors.

Data Analytics Leadership:

- Lead the analysis of structured and unstructured data sources to identify investment opportunities.

- Integrate multiple data sources including social media, expert opinions, analyst recommendations, and macroeconomic indicators.

- Develop and implement AI/ML models for pattern recognition in market behaviour

Team Leadership & Development:

- Build and lead a cross-functional team including data scientists, business analysts, and economics specialists.

- Foster collaboration between quantitative analysis and fundamental research teams.

- Establish processes for knowledge sharing and continuous improvement.

Strategic Analysis & Reporting:

- Conduct comprehensive scenario analyses considering various market conditions.

- Develop and maintain robust methodologies for evaluating investment opportunities.

- Present analytical findings and recommendations to senior leadership.

Critical Success Factors:

Quantitative Objectives:

- Achieve a 75% identification rate of multi-bagger stocks.

- Maintain a 75% hit rate on investment recommendations.

- Deliver 15% outperformance versus benchmark indices.

- Scan for Multi-baggers among Large Cap, Mid Cap and Small Cap sectors.

Operational Excellence:

- Successfully implement automated data collection and analysis systems.

- Establish repeatable processes for identifying investment opportunities.

- Create effective collaboration mechanisms between technical and investment teams.

Education and Experience:

- Full-time degree in Data Science.

- 7-10 years of experience combining data science with Equity Research.

- Proven track record in financial markets or investment analysis.

- Experience in leading cross-functional teams.

Essential Skills and Competencies:

Technical Expertise:

- Advanced proficiency in Python, R, or similar programming languages.

- Expertise in AI/ML frameworks and tools.

- Strong knowledge of financial data platforms (Bloomberg, Refinitiv).

- Experience with NLP, deep learning, and statistical modelling.

- Proficiency in time-series forecasting and quantitative finance.

Extensive experience in synthesising intelligence from diverse data streams, such as:

- Extract actionable insights from institutional investment managers' perspectives and strategies.

- Develop systematic approaches to capture and analyse investment thesis patterns across top-performing funds.

- Create frameworks to evaluate and incorporate peer investment strategies and market positioning.

- Develop methodologies to quantify and validate social media signals for investment decisions.

- Create algorithms to detect emerging market trends from digital channels.

- Synthesize insights from industry specialists, policy experts, and thought leaders.

- Develop frameworks to evaluate and integrate expert opinions into investment decisions.

- Create systematic approaches to validate and weight expert recommendations.

- Build systems to aggregate and analyse sell-side and buy-side research.

- Create algorithms to detect shifting market sentiments in research coverage.

- Design frameworks to integrate governmental data and policy changes into investment strategies.

- Develop models to evaluate macroeconomic impacts across different market segments.

- Create systematic approaches to identify leading indicators from government and regulatory data.

Leadership Competencies:

- Strategic thinking and business acumen.

- Team building and mentorship capabilities.

- Excellent communication and presentation skills.

- Strong project management abilities.

- Innovative problem-solving approach.