Data Analytics

Key Responsibilities:Enterprise Analytics Leadership:

• Define and execute the enterprise analytics and data strategy aligned with the organization's growth ambitions.

• Build a culture of data-driven decision-making across business functions including Distribution, Sales, Operations, Underwriting, Claims, Customer Experience, Finance, and HR.

• Establish analytics as a strategic business capability that drives revenue growth, expense optimization, risk management, and customer retention.

Data & AI Strategy:

• Lead the development and deployment of advanced analytics, AI, and machine learning solutions across the enterprise.

• Identify opportunities to leverage predictive analytics, GenAI, automation, and intelligent decisioning.

• Drive AI use cases including:

• Customer acquisition and retention

• Lead prioritization

• Persistency improvement

• Claims fraud detection

• Underwriting optimization

• Agent productivity enhancement

• Customer lifetime value modeling

Enterprise Data Management:

• Define data governance frameworks, data quality standards, and enterprise-wide data policies.

• Partner with technology teams to establish modern data platforms, data lakes, cloud analytics environments, and scalable architectures.

• Ensure accuracy, consistency, security, and regulatory compliance of enterprise data assets.

Business Partnership:

• Act as a strategic advisor to Executive Leadership, Business Heads, and Functional Leaders.

• Translate business challenges into scalable analytics and AI solutions.

• Drive measurable outcomes through data-driven interventions and performance management frameworks.

Analytics Centre of Excellence (CoE):

• Establish and lead a high-performing Analytics & Data Science CoE.

• Build capabilities across:

• Business Intelligence

• Data Engineering

• Data Governance

• Data Science

• Machine Learning

• Visualization & Storytelling

• Develop analytics talent and succession pipelines.

Technology Collaboration:

• Work closely with the CTO to shape the enterprise technology and data roadmap.

• Evaluate emerging technologies and vendors in analytics, cloud, data engineering, AI, and automation.

• Lead strategic investments in analytics platforms and data infrastructure.

Governance & Risk:

• Establish enterprise KPI frameworks and executive dashboards.

• Ensure compliance with regulatory and information security requirements.

• Drive responsible AI adoption and model governance practices.

Desired Outcomes:

The incumbent will be expected to:

• Establish a single source of truth for enterprise reporting.

• Improve customer retention and persistency through predictive interventions.

• Enhance sales productivity through analytics-led decision making.

• Reduce operational costs through automation and process analytics.

• Drive enterprise-wide adoption of AI and advanced analytics.

• Create measurable business impact through data monetization and insights.