Lead Data Scientist (12 Month Contract)

Chubb is a world leader in insurance. With operations in 54 countries and territories, Chubb provides commercial and personal property and casualty insurance, personal accident and supplemental health insurance, reinsurance and life insurance to a diverse group of clients. As an underwriting company, we assess, assume and manage risk with insight and discipline. We service and pay our claims fairly and promptly. The company is also defined by its extensive product and service offerings, broad distribution capabilities, exceptional financial strength and local operations globally. Parent company Chubb Limited is listed on the New York Stock Exchange (NYSE: CB) and is a component of the S&P 500 index. Chubb maintains executive offices in Zurich, New York, London, Paris and other locations, and employs approximately 40,000 people worldwide. Additional information can be found at: www.chubb.com.

Chubb celebrates diversity by fostering an inclusive, flexible and equitable workplace. We support applications from all members of our community and equitable access to our employment opportunities. We are open to discussing workplace flexibility in all our vacancies, to ensure we can attract the best candidates and accommodate individual needs, differences, disabilities and working arrangements. Please let us know if you require any adjustments to the recruitment process so we can support you to present your best self.

We’re looking for a Lead Data Scientist to join our Digital team and help deliver predictive analytics solutions that support pricing, underwriting, claims, and broader digital transformation initiatives.

In this role, you will:

Lead advanced analytics initiatives that support insurance portfolio management and commercial performance.

Apply statistical and machine learning techniques to generate insights that improve decision-making.

Work across structured, unstructured, and externally sourced data to identify opportunities and enrich analytical outcomes.

Extract, prepare, and analyse data using Python and other data management tools.

Develop predictive models and analytical solutions using techniques such as GLMs, gradient boosting, tree-based models, and other machine learning methods.

Partner with business teams to understand needs, scope analytical work, prioritise initiatives, and shape the analytics roadmap.

Collaborate with the Global Analytics team on model build and refinement, providing technical feedback and ensuring solutions meet business and quality standards.

Drive end-to-end delivery from problem framing and exploration through to model development, validation, deployment, and adoption.

Present analytical findings, model performance, and portfolio insights to business stakeholders, actuarial teams, and other key partners in clear, decision-ready language.

Maintain comprehensive documentation across the solution lifecycle, including business requirements, solution design, model logic, and validation outcomes.

Skills & experience

Bachelor’s or Master’s degree in data science, statistics, mathematics, actuarial science, computer science, engineering, or a related quantitative discipline.

5+ years of hands-on data science and advanced analytics experience.

Strong understanding of machine learning and statistical concepts, including supervised and unsupervised learning, GLMs, gradient boosting, anomaly detection, simulation, NLP, text analytics, and deep learning.

Strong technical proficiency in Python and common data science / machine learning libraries such as pandas, scikit-learn, statsmodels, XGBoost, LightGBM, PyTorch, or TensorFlow.

Practical experience with modern data platforms such as Databricks and Snowflake.

Experience working with structured and unstructured data, including sourcing, evaluating, and integrating external data.

Experience building and maintaining data and ML pipelines, with familiarity in MLOps practices such as deployment, monitoring, drift detection, and retraining.

Experience working in agile delivery environments, using tools such as Jira and Confluence to plan, prioritise, and track work.

Excellent communication and presentation skills, with the ability to engage actuarial, technical, and senior business stakeholders confidently.

Curious, analytical, and detail-oriented mindset, with strong ownership, problem-solving ability, and end-to-end delivery capability.

Experience in AI or model-driven analytics is highly regarded.

An actuarial background will be considered an advantage.

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