Head of Data

Prudential's purpose is to help people get the most out of life. We will deliver our purpose by creating a culture in which diversity is celebrated and inclusion assured, for our colleagues, customers, and partners. We provide a platform for our people to do their best work and make an impact to the business, and in exchange, we support our people's career ambitions. We pledge to make Prudential a place where you can Connect, Grow and Succeed.

We are seeking a highly skilled and visionary individual to join our team as the Head of Data and Analytics Innovation. As the leader of this critical function, you will be responsible for increasing data and analytics maturity in the organisation, driving the data and analytics strategy, fostering innovation and adapting new data and analytics solutions and practices. He/she will work closely with senior stakeholders, business and technology partners to identify, build and scale analytics solutions that contribute to company growth and drive value for customers.

**Roles & Responsibilities
Analytics Strategy & Roadmap
- Develop and execute a comprehensive analytics & AI strategy aligned with the company's business objectives and vision for innovation in the life insurance industry
- Build a strong network with stakeholders within Prudential to understand and identify analytics and AI opportunities and influence commercial decisions.
- Plan and manage analytics and AI roadmap for the organisation bridging business needs and technology capabilities.
- Set goals, success metrics for analytics team and translate objectives into revenue-driven metrics.
- Explore emerging trends, industry best practices, and technological advancements to stay ahead of the competition and position the company as a leader in data-driven decision-making.

Team Leadership and Development:

- Lead, grow and develop an analytics team responsible for analytics products development, driving actionable insights, tracking commercial performance, identifying opportunities and risks.
- Lead and develop of cross-functional relationships and foster collaboration among analytics product and engineering teams.
- Strengthen teams' skills in product thinking and promote a digital culture throughout the organisation. Leverage experience in product teams to transfer knowledge and introduce external perspectives.
- Maintain a high standard of product thinking and analytical depth within your team by appropriately hiring and mentoring individuals.

Analytics Product Development
- Identify opportunities for data-driven innovation and lead cross-functional projects aimed at improving products, services, and operational efficiency.
- Lead development of analytics products from ideation to deployment and manage high performing team of data scientists, engineers and analysts.
- Drive the implementation of best practices and consistency of standards in analytics product development

**Qualifications and Working Experience**
- Master’s degree in Data Science, Computer Science or a related filed. Advanced degrees preferred.
- Proven experience (8+ years) in data analytics, data science or related role in insurance or technology industry.
- Familiarity with end-to-end analytics solutions build and servicing, code writing, and code testing best practices is essential.
- Experience in translating business problems into data and analytics solutions and influencing senior stakeholders and business partners in their adoption.
- Excellent communication and presentation abilities to convey complex technical concepts to non-technical stakeholders.
- Technical background and ability to dive into details of technologies built by Engineering and Data Science teams. Possessing two-sided market strategy knowledge is a plus.
- Experience managing analytics teams, working collaboratively with Engineering and cross-functional partners, and guiding data-influenced product planning, prioritisation and strategy development.
- Proven track record of effectively leading high-performing teams in ambiguous situations. Skilfully assess shifting business priorities and align cross-functional teams to focus on the most critical tasks.
- In-depth knowledge of data analysis methodologies, machine learning, and statistical modelling techniques.
- Proficiency in programming languages and tools (e.g. Python, SQL, Spark)
- Strong understanding of cloud-based data platform and analytics technologies (e.g., Azure, Databricks, MLflow)
- Strong understanding of MLOps and managing a portfolio of ML models (CI/CD automation, workflow orchestration, reproducibility; versioning)
- Familiarity with Generative AI and experience in leveraging it in product development cycle would be an advantage