Company: Bank of England
Location: London
Job Description:
Overview
In this role you will apply data science to operational risk and resilience data to support supervisory judgement. You will curate internal and external datasets to generate actionable insights that inform supervisory priorities and cross-firm analysis. You will develop analytical tools, models and visualisations to improve consistency and decision-making, translating complex outputs for senior stakeholders. You will shape data-driven approaches across the division while managing imperfect data and promoting best practices.
Pay / Benefits
- non-contributory pension
- discretionary performance award
- 8% benefits allowance
- 26 days annual leave (+ up to 12 via benefits)
- private medical insurance
- income protection
Responsibilities
- Apply data science and advanced analytics to operational risk data to support supervisory judgement and decision-making.
- Identify, curate and exploit internal and external data sources to draw actionable insights for operational risk and resilience.
- Develop and maintain analytical tools, data models, dashboards and visualisations to improve insight and comparability of assessments.
- Frame analytical questions with experts to support supervisory priorities and ensure proportional use of data in firm assessments.
- Translate analytical outputs into clear, decision-relevant insights for supervisors and senior stakeholders.
- Assess imperfect, incomplete or noisy data, articulating limitations, assumptions and uncertainty.
- Influence data usage across the division by promoting good practice and reusable analytical assets.
- Own delivery of analytical products and shape priorities to promote data-driven approaches beyond current tasks.
Key requirements
- Experience applying data science, statistics, machine learning or advanced analytics to complex risk or regulatory problems.
- Experience developing analytical tools, data models, dashboards or visualisations to improve decision-making.
- Experience in data modelling, lifecycle management, sourcing, preparation, modelling, validation, visualisation and result communication.
- Experience with structured and unstructured data and extracting insights from imperfect data.
- Ability to build partnerships with subject matter experts to derive risk indicators.
- Ability to translate technical findings for non-technical stakeholders.
- Strong problem-solving, judgement under uncertainty, and collaboration with diverse teams.
- Experience framing analytical questions with SMEs to support evidence-based decisions.
- strong communication
- collaboration
- influencing
- data science
- advanced analytics
- data modelling
…
Posted: October 16th, 2026