• What you will doBuild statistical models on claims, policy and operational data, then help move useful outputs from prototype into business use.
• Write production-quality SQL and Python to extract, transform and analyse large datasets, supporting reliable insight and repeatable decision-making.
• Partner with data engineering to improve the data models and pipelines that support analysis, reporting and model development.
• Investigate business questions end to end: define the problem with stakeholders, prepare the data, complete the analysis and present clear recommendations.
• Automate recurring analysis and reporting to reduce manual repeat work and free the team to focus on new questions.
• Explain findings clearly to non-technical stakeholders, and be open to challenge where assumptions, conclusions or recommendations need to be tested.
• What we are looking forEssential1–2 years’ experience in a data science, analytics or quantitative role.
• A degree in a quantitative discipline (data science, computer science, statistics, mathematics, actuarial science, engineering or similar), or equivalent practical experience.
• Sound grasp of statistical fundamentals, and the judgement to know when a simple model beats a complex one.
• Clear written and verbal communication.
• The right to work in Ireland.
• Nice to haveExposure to insurance, financial services or another regulated industry.
• Strong Python for data work (pandas, scikit-learn or equivalent) and strong SQL.
• Version control and collaborative development practice (Git, code review).
• Experience with Azure, Databricks or other cloud environments.
• Familiarity with Power BI or a similar BI tool.
• Why join usSmall team, wide remit. You will not be handed one narrow slice of the pipeline.
• Real influence on how the data function gets built, rather than maintaining something built a decade ago.
• Close working relationship with actuarial and operational specialists, so you learn the insurance side properly.

LocationThis role is based in our Cherrywood office — the team works closely together and a lot of the learning happens by being in the room.