Data Scientist – Financial Services

About the role

You will join the Data Science team of a large, established financial services business that is investing heavily in its data platform and machine‑learning engineering capability. The focus is on moving models from the notebook into production to drive real business decisions.

Key responsibilities

• Own data‑science projects from problem definition through to production deployment.
• Collaborate with stakeholders to translate business challenges into clear data‑science use cases.
• Explore, clean, and prepare complex, large‑scale datasets.
• Build, test, and evaluate machine‑learning models using regression, classification, clustering and forecasting techniques.
• Write clean, reusable Python code that can transition beyond research.
• Work with ML Engineers to deploy models into a cloud environment.
• Define performance metrics and set up monitoring for live models.
• Review existing models, recommending retraining or replacement as needed.
• Communicate findings to both technical and non‑technical audiences.
• Help raise standards for experimentation, documentation, and model governance.

Required profile

• Commercial experience as a Data Scientist with end‑to‑end ML project delivery.
• Proficiency in Python and common data‑science libraries.
• Strong SQL skills and experience handling large datasets.
• Solid grounding in statistical modelling and machine‑learning concepts.
• Experience in feature engineering, model selection and evaluation.
• Ability to write clear, maintainable, testable code.
• Familiarity with Git‑based development workflows.
• Experience working in cloud platforms such as Azure, GCP or AWS.
• Excellent communication skills for explaining complex results.
• Financial services or insurance background is a plus but not required.

Required skills

• Python
• SQL
• Git
• Azure
• GCP
• AWS

What we offer

• Opportunity to see your models deployed and used across the organisation.
• Collaboration with dedicated ML Engineers, Data Engineers and technology teams.
• Work in a company that is building robust production‑ML capabilities.