Data Scientist – Underwriting Analytics

Zurich Insurance

Data Scientist specializing in Underwriting Analytics at Zurich Insurance. Leading complex analyses and developing predictive models for underwriting and pricing decisions.

Posted 6/5/2026part-timeDublin • 🇮🇪 IrelandJuniorMid-LevelWebsite

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

data pipelinesstatistical modellingmachine learningPythonRGeneralised linear modelsTree-based methodsTime series modellingClustering techniquesforecasting models

Soft Skills

leadershipownershipattention to detaildocumentationanalytical thinking

Tools & Technologies

Power BITableau

Industry Keywords

underwritingpricing decisionsadvanced analyticsfinancial forecastingperformance KPIs

Tech Stack

Tools & technologies

PythonTableau

About the role

Key responsibilities & impact

Leading the development of analytical solutions and models to support underwriting and pricing decisions.
Designing, building and maintaining robust data pipelines, datasets and codebases that support regular MI, dashboards and advanced analytics.
Taking ownership of more complex reporting and forecasting processes, ensuring they are accurate, scalable and well-documented.
Enhancing existing MI for Underwriting and Performance KPIs, introducing more advanced analytics.
Developing and maintaining financial and performance forecasting models to support planning and portfolio steering.

Requirements

What you’ll need

Bachelor’s degree (or equivalent) in Mathematics, Statistics, Actuarial Science, Data Science, Computer Science, Engineering, Economics, Finance or a related quantitative discipline.
Typically 2–5 years of experience in data science, advanced analytics, actuarial, or a closely related field.
Strong coding skills in at least one of Python or R.
Practical experience with statistical modelling and/or machine learning techniques, such as: Generalised linear models (GLMs) or other regression methods, Tree-based methods (e.g. random forests, gradient boosting), Time series modelling and forecasting, Clustering and segmentation technique
Experience with data visualisation tools such as Power BI or Tableau.

Benefits

Comp & perks

Flexible working models
Opportunities for further training & development