About the Role: Lending Analytics & Data Science

We are looking for a senior analytics leader with deep experience in lending, credit analytics, retail finance, and data science to lead the analytics agenda across Home Loans, Business Loans and Gold Loans.

The incumbent will work closely with lending business heads and functional leaders to leverage customer, bureau, transaction, behavioural and portfolio data to drive:

- Business Growth: Accelerate customer acquisition, lead conversion, disbursements and portfolio growth through data-led targeting and decisioning.

- Customer Intelligence: Build a comprehensive understanding of customer profiles, borrowing behaviour, financial needs and cross-product opportunities.

- Credit & Portfolio Quality: Strengthen underwriting intelligence, risk segmentation, early warning indicators and portfolio monitoring to ensure sustainable growth.

- Sales & Distribution Effectiveness: Improve lead prioritization, sourcing productivity, funnel conversion and channel performance through predictive analytics.

- Customer Lifetime Value: Identify opportunities for repeat borrowing, upsell, cross-sell and deeper customer relationships across lending products.

Key Responsibilities

- Define Lending Analytics Strategy: Own and drive the end-to-end analytics strategy across Home Loans, Business Loans and Gold Loans, translating business priorities into measurable analytical outcomes and growth initiatives.

- Customer Acquisition & Lead Analytics: Build predictive models and customer segmentation strategies for lead generation, lead scoring, propensity-based targeting, customer profiling and acquisition optimization.

- Funnel & Conversion Analytics: Drive analytics across the complete lending funnel, from lead sourcing and login to approval, sanction and disbursement. Identify bottlenecks and improve conversion rates and turnaround times.

- Credit Risk & Underwriting Analytics: Partner with Credit and Risk teams to develop predictive models and analytical frameworks for credit assessment, borrower segmentation, underwriting support, risk-based pricing and portfolio quality monitoring.

- Portfolio Analytics & Early Warning Systems: Develop portfolio monitoring frameworks, early warning indicators, delinquency prediction models and risk dashboards to proactively identify emerging portfolio risks and support collections strategies.

- Product-specific Analytics: Develop tailored analytical frameworks for HL, BL and GL, considering their distinct customer segments, sourcing channels, collateral characteristics, underwriting processes and risk parameters.

- Cross-sell & Customer Lifetime Value: Leverage customer 360 insights, bureau data, transaction behaviour and existing product relationships to identify cross-sell opportunities, repeat borrowing potential and customer lifetime value.

- Sales & Channel Productivity: Build analytics-led frameworks to evaluate sourcing channel effectiveness, sales productivity, branch performance, partner performance and distribution efficiency.

- Advanced Analytics & AI-led Decisioning: Drive the development and deployment of ML models across acquisition, credit risk, propensity, churn, collections and customer lifetime value. Leverage AI/GenAI to automate reporting, generate insights and enable faster business decision-making.

- Data Infrastructure & Customer 360: Partner with Data Engineering and Technology teams to establish scalable data pipelines, feature stores, customer-level datasets and integrated lending dashboards.

- Performance Measurement & Governance: Define business and analytics KPIs, establish measurement frameworks, monitor model performance, ensure model explainability, manage model drift and maintain regulatory and Responsible AI standards.

- Stakeholder Management: Work closely with Lending Business Heads, Product, Credit, Risk, Technology, Marketing, Finance and Operations to embed analytics into business decisions and customer journeys.

- Team Leadership: Build, lead and develop a high-performing lending analytics and data science team. Drive technical excellence, stakeholder management, analytical rigour and a culture of data-led decision-making.

Candidate Requirements

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

- 12 - 15+ years of experience in analytics, data science or business intelligence, preferably within banking, NBFCs, housing finance or fintech lending businesses.

- Strong domain expertise in retail lending, with exposure to Home Loans, Business Loans, Gold Loans or other secured lending products.

- Demonstrated experience in building and deploying predictive models across customer acquisition, lead scoring, credit risk, underwriting, portfolio monitoring, collections and cross-sell.

- Strong understanding of lending lifecycle, credit bureau data, customer segmentation, underwriting processes, delinquency metrics and portfolio quality.

- 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.