- Support life and health experience studies, including mortality, morbidity, lapse and claims analysis.
- Perform data validation, exposure calculations, actual-versus-expected analysis, and investigation of experience trends.
- Apply actuarial and statistical techniques to support assumption reviews and experience analysis.
- Interpret and communicate study findings through reports, presentations and visualisations.
- Develop and maintain actuarial analytics and experience study workflows using Databricks, SQL, Python and PySpark.
- Prepare, reconcile and analyse policy, exposure and claims data, investigating data quality issues and their impact on results.
- Support the enhancement and modernisation of actuarial tools, models and analytical processes.
- Collaborate with actuarial, business and technology teams to deliver scalable and controlled analytical solutions.
- Identify opportunities to improve actuarial processes through automation, workflow optimisation and standardisation.
- Support process improvement initiatives, including process mapping, testing, implementation and user adoption.
- Explore and assess AI and machine learning applications to enhance actuarial analysis and operational efficiency.
- Ensure solutions comply with applicable governance, data privacy, information security and AI risk management requirements.
Your Profile
- Degree in Actuarial Science, Mathematics, Statistics, Computer Science or another quantitative discipline.
- Progress towards Fellowship of a recognised actuarial qualification.
- Relevant actuarial experience within life and health insurance or reinsurance.
- Experience in experience studies, assumption setting, pricing, valuation or related actuarial functions.
- Strong technical skills in Databricks, SQL and Python; experience with Spark/PySpark is advantageous.
- Experience working with large and complex datasets and analytical workflows.
- Solid understanding of statistical methods and the ability to apply actuarial judgement to business problems.
- Experience developing, evaluating or implementing AI and/or machine learning solutions in a business environment.
- Strong analytical, problem-solving and communication skills.
- Ability to explain technical concepts to both technical and non-technical stakeholders.
- Ability to work independently while collaborating effectively across teams and locations.
Nice to Have
- Experience conducting life and health experience studies across multiple products, portfolios or markets.
- Knowledge of machine learning, credibility theory or advanced statistical techniques.
- Experience with Databricks capabilities such as Delta Lake, Unity Catalog, Workflows or MLflow.
- Exposure to large language model (LLM) applications, retrieval-augmented generation (RAG), API integration or natural language analytics.
- Experience in process improvement methodologies, workflow automation or change implementation.
- Relevant Databricks, analytics or cloud certifications.