Senior Data Scientist

About the role

Are you a motivated and innovative person, interested in shaping advanced analytics, machine learning and AI solutions for one of the largest global insurers? As a Senior Data Scientist, you will play a key role in transforming data into actionable insights, predictive models and AI‑driven solutions that support strategic decision‑making across Zurich.

Key responsibilities

• Develop and deploy statistical, machine learning and AI models to support strategic business objectives.
• Design and maintain forecasting solutions for key customer, operational and financial KPIs using time‑series and predictive modelling techniques.
• Build customer intelligence solutions, including segmentation, lifetime value estimation, retention and churn prediction.
• Apply machine learning techniques to fraud detection, claims analytics and risk assessment.
• Explore large and complex datasets to identify trends, patterns, risks and opportunities.
• Design and run experiments such as A/B tests to measure the impact of data‑driven initiatives.
• Collaborate with business stakeholders to define use cases and communicate analytical findings.
• Support deployment, monitoring and continuous improvement of machine learning models in production.

Required profile

• 6+ years of experience in data science, advanced analytics and machine learning.
• University degree in Statistics, Mathematics, Computer Science, Engineering, Data Science or related quantitative discipline.
• Strong foundation in statistical modelling, predictive analytics and machine learning techniques.
• Practical experience with forecasting methods, particularly time‑series modelling.
• Strong programming skills in Python and/or R, as well as SQL.
• Experience working with large, complex datasets and data pipelines in a production environment.
• Excellent communication skills with the ability to explain complex analytical concepts to non‑technical audiences.
• Fluent written and spoken English.

Required skills

• Python
• R
• SQL
• Statistical modelling and predictive analytics
• Machine learning and time‑series forecasting
• Azure cloud analytics ecosystem
• Databricks and Spark
• MLOps and model deployment frameworks
• Responsible AI and model risk management practices