Data Analytics

Key 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:

1. Customer acquisition and retention

2. Lead prioritization

3. Persistency improvement

4. Claims fraud detection

5. Underwriting optimization

6. Agent productivity enhancement

7. 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:

1. Business Intelligence

2. Data Engineering

3. Data Governance

4. Data Science

5. Machine Learning

6. 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.

(ref:hirist.tech)