1. Arun, could you start by sharing your journey into data science? What drew you to this field, and how has your career evolved across different industries?

2. In your work at Swiss Re, you’ve developed solutions like geospatial property databases and electric vehicle pricing models. Could you share a real-world example of how these solutions impacted decision-making?

3. The insurance industry is often considered traditional. How is data science reshaping this space, particularly in areas like claims management, risk assessment, and customer retention?

4. You’ve also worked in the healthcare space, including projects at Novartis. Could you give an example of how you applied data science to address challenges in healthcare or pharmaceutical domains?

5. Predictive analytics and anomaly detection are common terms in your work. Could you explain what they mean in practical terms and provide examples of their application?

6. Data quality and governance are critical in analytics. How do you ensure these are maintained, especially when dealing with large datasets across multiple platforms?

7. AI is transforming analytics. How do you see AI enhancing data science, and what opportunities or challenges do you foresee in its application to industries like insurance and healthcare?

8. From your experience, what are the key challenges organisations face when trying to scale their data-driven initiatives, and how can they overcome them?

9. You’ve mentored data science students and teams. What skills or mindsets do you believe are essential for someone to succeed in this field?

10. Finally, for listeners considering a career in data science, what advice would you give to help them navigate this ever-evolving industry?