Data Science Lead

About the role

Zurich Capability Center is seeking a Data Science Lead to drive the development and delivery of advanced analytics solutions. You will guide a team of data scientists, translate business problems into data‑science use cases, and ensure high‑impact insights for the insurance business.

Key responsibilities

• Understand business problems and design analytical approaches, engaging with stakeholders to define use cases.
• Lead proof‑of‑concept development, decide which solutions to scale into production.
• Oversee data preparation, ensuring quality, consistency, and reusability of datasets.
• Generate risk insights, challenge existing views with data‑driven evidence.
• Build and implement predictive models using structured and unstructured data, coaching team on best practices.
• Collaborate with Data Engineers, ML/AI Ops, and IT to deploy models, define monitoring frameworks, and maintain performance.
• Communicate findings and model performance to business stakeholders.
• Mentor junior data scientists and promote a culture of experimentation and compliance.

Required profile

• Bachelor’s degree or higher in a quantitative field (e.g., Mathematics, Statistics, Computer Science, Engineering, Economics).
• Several years of hands‑on experience delivering data‑science models into production.
• Proven ability to lead projects or small teams, prioritize use cases, and coordinate cross‑functional stakeholders.
• Strong verbal and written communication skills, able to translate technical results for business audiences.
• English proficiency at B2 level or higher; additional languages are a plus.
• Experience in insurance or financial services, especially in risk, pricing, claims, or customer analytics, is advantageous.

Required skills

• Python and standard data‑science libraries (NumPy, pandas, scikit‑learn, PyTorch, TensorFlow).
• Experience with cloud platforms and MLOps tools (Azure, Databricks).
• SQL and additional programming languages such as R, Scala, or Java.
• Knowledge of machine‑learning and deep‑learning techniques, including GBM, Random Forest, CNN, transformers, and large language model fine‑tuning.