About the Role: Analytics & Data Science - Investments

We are looking for a senior analytics and data science leader with strong experience in investments, mutual funds, wealth management, or financial services analytics.

You will help ABCD and ABC One Analytics unlock growth in the Investments business by leveraging customer data, investment behaviour, transaction patterns, and predictive modelling to:

- Accelerate customer acquisition, investment product penetration, SIP adoption, upsell and cross-sell through smarter targeting and decisioning.

- Monetize insights through a superior understanding of customer investment preferences, financial goals, risk appetite, and life-stage needs.

- Drive investor engagement, repeat investments, portfolio expansion, and customer lifetime value.

- Deliver hyper-personalized investment recommendations and experiences across channels, products and customer journeys.

Key Responsibilities:

- Define the customer analytics strategy for the Investments business, translating business priorities into measurable analytics outcomes across mutual funds, SIPs, investment products and related offerings.

- Lead end-to-end customer acquisition and investment cross-sell programs: data enrichment, feature engineering, model development, strategy design, deployment and ongoing optimization.

- Build target cohorts and next-best-action/offer decisioning for investment campaigns across channels (app/web, telesales, email, SMS, branch, partners).

- Develop, validate and monitor ML models (propensity, churn, LTV, investment propensity, SIP conversion, repeat investment and NBO/NBA), ensuring robustness.

Candidate Requirements:

- Bachelor's/Master's degree in Mathematics, Statistics, Economics, Engineering, Computer Science or a related quantitative discipline.

- 9 - 12 years of experience in analytics, data science or business intelligence, preferably within banking or fintech investments businesses.

- Strong domain expertise in mutual funds, stocks, bonds, fixed deposits, NPS, etc.

- Demonstrated experience in building and deploying predictive models across customer acquisition, customer segmentation and cross-sell.

- Hands-on expertise in Python, SQL, PySpark and advanced statistical/machine learning techniques, with experience across cloud-based data platforms and enterprise data environments.

- Experience in developing scalable analytical solutions, decisioning engines, dashboards and automated reporting frameworks.

- Strong understanding of AI/ML applications in lending, including explainable AI, model governance, Responsible AI and GenAI-led analytics automation.

- Proven experience leading large analytics/data science teams and managing complex, cross-functional business mandates.

- Ability to influence senior business stakeholders, translate business challenges into analytical solutions and drive measurable business outcomes.