As Senior Data Science & Analytics Lead, you will play a pivotal role in evolving our data function from strong management information delivery to a data science‑led, AI‑enabled capability that drives measurable improvements in decision‑making, operational efficiency and risk management. You will be accountable for developing and deploying predictive and statistical models across complex datasets, embedding advanced analytics and AI into core business processes to move from insight generation to automated, data‑driven decisioning. This includes ensuring solutions transition effectively from experimentation into robust, controlled and scalable production use. Combining strong people leadership with technical credibility, this role will build sustainable data science capability within the team, ensuring experimentation, modelling and AI adoption aligned to business priorities and governance expectations.
Responsibilities
- Lead a team of analysts, supporting performance, development plans, and day‑to‑day prioritisation.
- Act as a role model for modern analytics practices, curiosity, and outcome‑focused delivery.
- Build and mature analytical and AI capability within the team, coaching analysts toward more advanced analytics, automation, and AI techniques.
- Partner with senior stakeholders across the UK Life Business and supporting functions to identify opportunities for deeper insight, automation, or improved decision‑making that deliver clear, actionable outcomes.
- Translate complex business questions into analytical approaches that deliver clear, actionable outputs.
- Own a prioritised insight and automation development pipeline aligned to business value and capacity.
- Lead the design and delivery of advanced analytics and data science‑driven solutions, including predictive insight, trend analysis, and decision‑support models.
- Shape and oversee the responsible use of AI and GenAI to automate insight generation, controls, and repeatable MI processes.
- Act as the accountable lead for exploratory AI and advanced analytics use cases, ensuring appropriate governance, ethics, and risk considerations are met.
- Guide the transition from pilot and proof‑of‑concept into robust, scalable BAU solutions in partnership with Data Engineering, Governance and Architecture.
- Embed model lifecycle thinking, including documentation, validation, monitoring, and ongoing performance management where applicable.
- Embed standards for documentation, validation, and handover into BAU.
- Champion efficient tooling (Power BI, Power Platform, SQL, Python) and automation‑first thinking.
- Promote reuse, simplification, and scalability across the reporting estate.
- Embed experimentation frameworks (controlled testing, monitoring, feedback loops) to safely trial and scale advanced analytics and AI‑enabled solutions.
Qualifications
- Strong experience applying advanced analytics or data science‑led approaches to ambiguous business problems within Financial Services or similarly regulated environments.
- Proven ability to lead and develop analysts while remaining technically credible.
- Hands‑on experience building and deploying predictive or statistical models in a business environment.
- Strong working knowledge of Python (or equivalent) for data analysis and modelling.
- Experience designing and applying experimentation techniques (A/B testing, hypothesis testing).
- Advanced skills in SQL and Power BI, including automation of data and MI workflows.
- Experience delivering analytical solutions from prototype into production environments.
- Confident communicator, able to influence stakeholders without relying on hierarchy.
- Experience working within structured delivery practices (agile/iterative delivery) including backlog and governance management using Azure DevOps or equivalent.
- Experience scaling data science or analytics capability within a team or function.
- Exposure to cloud‑based data platforms or modern data ecosystems (Snowflake, AWS, Azure).
- Experience applying AI/GenAI techniques in a business context, including automation of insight generation or decisioning.
- Strong understanding of data governance, controls and regulatory considerations within Financial Services.
- Experience working in cross‑functional delivery models with Data Engineering, Architecture and Governance teams.
- Degree (or equivalent experience) in a numerate or analytical discipline such as mathematics, statistics, data science, computer science, engineering, or a related field.
- Professional qualifications, certifications, or structured learning in analytics, data science, automation, or AI (cloud data platforms, analytics engineering, machine learning, or responsible AI).
- Commitment to continuous professional development, including keeping pace with emerging analytics, automation, and AI practices, and supporting the learning of others through coaching, apprenticeships, or structured development pathways.
Benefits
- 12% defined non‑contributory pension scheme.
- Annual company bonus.
- Income Protection.
- Life cover - four times your salary.
- 28 days holiday a year plus bank holidays.
- Option to buy up to an additional 20 days or sell some of your holiday.
- Three days paid volunteering.
- Up to 16 weeks’ full pay for maternity, paternity and adoption leave.
- Access to private medical insurance.
- Virtual GP appointments.
- Discounted gym membership.
- Free flu jab.
- Access to wellbeing partners support.
As an inclusive employer we also want to ensure that all candidates feel comfortable and are able to perform at their best during the interview. You'll have the opportunity to let us know of any reasonable adjustment or practical support needed when you apply.
