Data Scientist

Overview

In this Data Scientist role, you will build advanced analytical models using consumer and market data to identify emerging risks and harm signals. You’ll deploy scalable, automated analytics to support regulatory decisions and consumer protection. You will work within the Digital Intelligence Solutions team to turn insights into practical recommendations for cross-functional stakeholders. This is a chance to shape how the FCA surfaces evidence-based intelligence and improves regulatory outcomes at scale.

Pay / Benefits

• 25 days annual leave plus bank holidays
• Non-contributory pension and life assurance
• Private healthcare with Bupa
• Income protection and 24/7 Employee Assistance
• 35 hours of paid volunteering annually
• Flexible benefits scheme

Responsibilities

• Develop advanced analytical models to detect emerging risks and consumer harm signals from structured and unstructured data
• Design, test and deploy scalable analytics that strengthen consumer intelligence activities
• Automate data workflows to increase efficiency and repeatability
• Build and maintain analytical products and dashboards to support evidence-based decision making
• Validate robust, explainable methodologies suitable for regulatory use
• Collaborate with stakeholders to translate complex findings into actionable recommendations
• Support data governance, data protection and regulatory compliance activities while adhering to governance standards

Key requirements

• Experience applying statistical analysis and data science techniques to solve complex business problems
• Experience interpreting large datasets using Python, SQL and related technologies
• Experience developing dashboards and analytical reports for diverse stakeholders
• Knowledge of data science methods including statistical modelling, predictive analytics, machine learning and NLP
• Ability to translate ambiguous problems into structured analytical approaches
• Stakeholder engagement to shape decisions and deliver outcomes
• Understanding of data governance, data quality and regulatory considerations in handling sensitive data

• Stakeholder engagement
• Clear communication of complex findings
• Critical thinking and problem solving
• Python programming with data processing libraries
• SQL and related data technologies
• Statistical modelling, predictive analytics and machine learning

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