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Data Scientist - PRA

Job in Leeds, West Yorkshire, ME17, England, UK
Listing for: Bank of England
Full Time position
Listed on 2026-09-20
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Data Science Manager, Data Analyst, Data Engineering
Salary/Wage Range or Industry Benchmark: 51000 - 57000 GBP Yearly GBP 51000.00 57000.00 YEAR
Job Description & How to Apply Below

The Authorisations, Regulatory Technology and International Supervision (ARTIS) Directorate brings together the PRA's Authorisations Division, the Reg Tech, Data and Innovation Division, and four divisions supervising UK activities of internationally headquartered banks, custodians and designated investment firms. It provides global leadership through data analytics, technical capability, consistent supervision and regulatory transactions, working collaboratively across the PRA and wider Bank.

The Cross Firm Team (CFT)

The CFT provides cross-firm financial analysis, risk identification, and thematic reviews across the international banking section. Combining supervisory insight with cutting-edge analytics, the team supports supervisors, seniors, and the wider Bank in identifying emerging risks, understanding industry trends, and taking informed supervisory action.

The ARTIS Data Science Spoke (ADSS)

ADSS is a team of dedicated data scientists within the CFT developing innovative artificial intelligence (AI), machine learning (ML), and advanced analytics capabilities to deliver more effective and efficient use of data within ARTIS and ultimately drive better decision making.

Job Description

The role holder will sit at the intersection of AI implementation and supervision and lead contributions to the development of advanced analytics capabilities. They will interface with data scientists, supervisors, and technical experts to ensure that ADSS is best serving the data needs of ARTIS. As a result, they will gain insight into how international banks operate, and are supervised, as well as sitting at the forefront of AI implementation in the PRA.

A key part of ADSS' ongoing workplan is to maximise opportunities opened by the PRA's future data product, Supervisory Analytics and Metrics (SAM). SAM sits on the Enterprise Data Platform (EDP), and the role holder will play a leading role in assessing and implementing cloud-based AI solutions, including pipelines and products spanning Azure Data Factory and Databricks which include state-of-the-art AI & ML techniques.

Number

of direct reports: 0 Role Responsibilities
  • Lead the design and development of cutting-edge AI tools and products to make better use of supervisory data.
  • Work with supervisors and technical specialists to understand analytics gaps and design the innovative future data landscape within ARTIS to meet them.
  • Lead contributions to strategic Bankwide and PRA initiatives such as the Enterprise Data Platform (EDP) and Supervisory Analytics and Metrics (SAM).
  • Keep abreast of frontier AI developments to constantly assess and explore new opportunities to innovate alongside other PRA data scientists.
  • Build and maintain strong collaborative relationships with key partners at all levels. This may include representing ADSS in presentations to senior committees.
Role Requirements
  • Experience contributing to projects that apply AI or ML techniques to drive more effective and efficient use of data.
  • Experience developing data pipelines using best practices in data engineering, AI, and ML.
  • Ability and willingness to proactively seek out opportunities to innovate.
  • Keen interest in learning how investment banks operate, how international banks are supervised, and general banking business models.
  • Proficiency in Python and/or R, or their Spark equivalents.
  • Excellent stakeholder management skills, and the ability to communicate technical concepts to non-technical audiences.
  • Strong problem-solving skills, and the ability to deliver under pressure.
  • Ability and willingness to stay updated on frontier AI developments.
  • Experience working with solutions that involve Large Language Models (LLMs) and agentic approaches, including evaluating the efficacy of them.
  • Experience…
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