Data Science Analytics - Loans/ Credit Card !

Role & responsibilities

• Business Analytics & Strategy
• Develop and execute analytics strategies to drive growth across unsecured lending and credit card businesses.
• Generate actionable insights on customer acquisition, portfolio performance, risk behavior, and profitability.
• Partner with Product, Business, Risk, and Technology teams to drive data-led decision making.
• Identify opportunities to improve customer conversion, engagement, retention, and cross-sell outcomes.
• Data Science & Machine Learning
• Lead the development and deployment of predictive models across the lending lifecycle.
• Build and enhance models related to customer acquisition, propensity, cross-sell, churn, fraud, and portfolio management.
• Drive implementation of ML/AI-based solutions to improve business performance and customer experience.
• Ensure continuous monitoring, validation, and refinement of analytical models.
• Portfolio & Customer Analytics
• Analyze customer behavior, transaction patterns, and portfolio trends to improve portfolio quality and profitability.
• Design segmentation frameworks and customer targeting strategies.
• Monitor key portfolio metrics and recommend corrective actions based on trends and emerging risks.
• Stakeholder Management
• Influence senior leadership through data-driven insights and strategic recommendations.
• Collaborate closely with Product, Risk, Digital Banking, Marketing, and Technology teams.
• Present analytical findings and business recommendations to executive stakeholders.
• Technology & Analytics Enablement
• Drive adoption of modern analytics tools, cloud-based analytics platforms, and automation frameworks.
• Promote best practices in data governance, model development, and analytics delivery.
• Mentor and build high-performing analytics teams.

Preferred candidate profile

• 8-15 years of experience in Analytics, Data Science, Digital Lending, Cards Analytics, or Consumer Finance Analytics.
• Strong experience within Unsecured Lending and/or Credit Cards domain is mandatory.
• Experience in Digital Banking, Fintechs, NBFCs, Credit Card issuers, or Retail Banking analytics functions preferred.
• Strong knowledge of machine learning techniques and predictive modelling.
• Hands-on experience with analytics tools such as Python, SQL, SAS, R, Power BI, Tableau, or similar.
• Deep understanding of customer analytics, portfolio analytics, decision science, and data modelling.
• Experience working with large datasets and modern data platforms.
• Strong problem-solving and analytical skills.
• Strategic thinking with business acumen.
• Ability to translate complex analytics into business outcomes.
• Excellent stakeholder management and communication skills.