Senior Risk Analyst (Data Scientist) in Operational Risk and Capital Intelligence

Overview

In this role you apply data science and quantitative analysis to operational risk and resilience data to support supervisory decisions. You will extract insights from diverse datasets and build analytical tools and dashboards to inform risk assessment. You’ll collaborate with risk, technology, and capital specialists to advance evidence-based decision-making and contribute to the division’s data strategy. This is a hands-on position at the heart of supervisory analytics with impact on the UK financial system.

Pay / Benefits

• Non-contributory, career-average pension with options to adjust
• Discretionary performance award
• 8% benefits allowance
• 26 days’ annual leave (+ up to 12 extra via benefits)
• Private medical insurance
• Income protection

Responsibilities

• Apply data science and analytical techniques to operational risk data to support supervisory assessments
• Identify, collate and analyse internal and external datasets to generate insights for operational risk and resilience
• Develop and maintain tools, dashboards, visualisations and models to support evidence-based supervisory assessments
• Collaborate with risk, resilience, technology risk and capital specialists to formulate analytical questions
• Produce high-quality outputs and communicate findings to technical and non-technical audiences
• Apply appropriate methodologies to imperfect or complex data and maintain analytical quality
• Contribute to cross-firm analysis and the division’s data toolkit, promoting good analytical practice
• Stay aware of new data science techniques and identify opportunities to apply them to supervisory challenges
• Lead discrete analytical workstreams and help shape the division’s data strategy

Key requirements

• Experience applying data science or quantitative analysis to business, risk, regulatory or policy problems
• Working knowledge of the data science lifecycle (data acquisition, preparation, analysis, visualisation, interpretation)
• Experience with analytical/visualisation tools such as Python, R, SQL, Power BI or equivalent
• Experience working with structured and unstructured datasets to identify trends and insights
• Ability to communicate technical findings to non-technical audiences
• Strong analytical and problem-solving skills under uncertainty
• Experience collaborating with subject matter experts to support evidence-based decision-making
• Ability to work independently and prioritise competing demands
• Demonstrated curiosity to explore new analytical techniques and data sources

• Strong communication and stakeholder engagement
• Ability to explain complex concepts clearly to diverse audiences
• Collaborative and multidisciplinary team working
• Python
• R
• SQL

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