You will lead Business Analytics for Aditya Birla Capital Digital (ABCD), owning the end-to-end customer analytics agenda across acquisition, cross-sell, and omni-channel personalization. This is a leadership role requiring strong hands-on capability in data science and decisioning, plus the ability to influence senior stakeholders and build a high-performing team.

About Aditya Birla Capital :

Aditya Birla Capital Limited (ABCL) is the holding company for the financial services businesses of the Aditya Birla Group. With subsidiaries/JVs that have a strong presence across Protecting, Investing and Financing solutions - ABCL is a financial solutions group that caters to the diverse needs of its customers across their life cycle. Powered by more than 34,000 employees, the businesses of ABCL have a nationwide reach with over 1,200 branches, more than 2,00,000 agents/channel partners and several bank partners.

Aditya Birla Capital Limited is a part of the Aditya Birla Group, which is in the league of Fortune 500. Anchored by an extraordinary force of over 140,000 employees, belonging to 100 nationalities, the Aditya Birla Group operates in 36 countries across the globe.

About the Role : Analytics & Data Science

We are looking for a senior analytics and data science leader with experience in lending, insurance, mutual funds, or payments. You will help ABCD and ABC One Analytics unlock growth by leveraging customer data to :

- Accelerate acquisition, upsell and cross-sell through smarter targeting and decisioning

- Monetize insights with a superior understanding of customer needs across life stages

- Deliver hyper-personalized experiences across channels, products and journeys

Key responsibilities :

- Define the customer analytics strategy across lending, insurance, mutual funds and paymentstranslating business priorities into measurable analytics outcomes.

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

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

- Develop, validate and monitor ML models (propensity, churn, LTV, NBO/NBA), ensuring robustness, explainability, performance drift management, and Responsible AI practices (privacy, fairness, and regulatory compliance).

- Drive an AI-first analytics approachleverage GenAI/AI tools to accelerate insight generation, automate routine analysis, and enable self-serve decisioning (e.g., natural-language querying, AI-assisted reporting) with clear guardrails.

- Partner with data engineering to build a 360 customer view and scalable datasets/feature stores across touchpoints and journeys.

- Define KPIs and measurement frameworks; build dashboards and reporting to track funnel, campaign, and channel performance.

- Design experiments (A/B, multivariate) and championchallenger frameworks; quantify impact and drive iteration based on results.

- Collaborate with product, engineering, marketing, risk, and finance to prioritize use-cases, embed analytics into journeys, and drive business outcomes.

- Build and lead a high-performing analytics teamhiring, mentoring, and setting high standards for delivery, stakeholder management, and technical rigor.

What you bring :

- Bachelors/Masters in Mathematics, Statistics, Engineering, or a related quantitative discipline.

- 10 to 12 years in analytics/data science in financial services, with strong exposure to customer, marketing/channel, campaign, and/or payments analytics.

- Hands-on with Python, SQL and PySpark; experience working on cloud platforms as well as legacy data environments.

- AI-first mindset : experience building AI-assisted analytics tools and ability to implement safe, compliant patterns for using GenAI with enterprise data.

- Comfort with ambiguity; ability to translate business problems into analytical approaches, rapidly prototype solutions, and automate repeatable workflows.

- Proven experience leading and developing teams of data scientists/decision scientists/analysts.

- Strong communication skills to influence non-technical stakeholders with clear storytelling and data-driven recommendations.