Deputy Vice President

Role & responsibilities

Brief Summary of the Role

To lead the design, development, and deployment of advanced analytical models for predicting health risks, claims utilization, fraud detection, customer behavior, and business outcomes. The role focuses on leveraging machine learning, statistical modelling, and AI techniques to generate actionable insights that drive profitable growth, risk management, and customer engagement for the health insurance business.

Key Responsibilities

Predictive Modelling & Advanced Analytics

• Lead the development of predictive models for claims forecasting, disease risk stratification, customer lifetime value, fraud/waste/abuse detection, and lapse/persistency.
• Design machine learning and AI algorithms to address underwriting, pricing, health risk management, and operational efficiency.
• Build and validate statistical models (GLMs, survival analysis, decision trees, ensemble methods, deep learning).
• Implement morbidity and utilization models to identify high-risk, high-cost populations.
• Develop Model on Population Health Analytics
• Develop propensity models to support cross-sell, up-sell, and targeted health programs.

Business Application of Analytics

• Collaborate with underwriting, actuarial, claims, product, and wellness teams to embed models into decision-making processes.
• Provide insights to support product pricing, portfolio optimization, and reserving.
• Partner with clinical teams to design disease management and preventive health programs based on model outcomes.
• Enable digital transformation by integrating predictive models into customer journeys, CRM, and digital platforms.

Data Management & Tools

• Work with large, complex healthcare datasets
• Oversee data preparation, feature engineering, and variable selection for modelling.
• Ensure data quality, governance, and compliance with IRDAI and privacy standards.
• Deploy models using modern tools and cloud environments.

Stakeholder Engagement

• Lead a team of data scientists and analysts, guiding them on methodology, coding practices, and business application.
• Translate technical results into business insights for leadership and non-technical stakeholders.
• Present findings, ROI impact, and recommendations to senior management and CXOs.
• Stay updated on emerging AI/ML techniques and their application in health insurance.

Skills & Competencies

• Strong understanding of health insurance domain claims, underwriting, risk management, fraud detection, and actuarial linkages.
• Proficiency in Python, R, SQL, and machine learning libraries
• Knowledge of statistical methods (GLM, regression, clustering, time-series, survival models).
• Experience with BI and visualization tools (Power BI, Tableau).
• Strong business acumen and ability to apply analytics to solve real-world business problems.
• Excellent communication, stakeholder management, and team leadership skills.

Preferred candidate profile -

Qualifications & Experience

• Master's/PhD in Statistics, Data Science, Actuarial Science, Computer Science, or related quantitative discipline.
• 10-12 years of experience in Data Science/Advanced Analytics, with at least 4–5 years in health insurance/healthcare/financial services.
• Proven track record in building and deploying predictive models at scale.
• Experience in leading analytics teams and delivering measurable business impact.