Data Scientist in Technical Capability Team

Data Scientist in Technical Capability Team

The Authorisations, RegTech and International Supervision (ARTIS) Directorate within the PRA is formed of:

• The Authorisations Division, which deals with regulatory transactions where firms apply to carry out PRA regulated activities or make changes to the activities they undertake.
• The RegTech, Data and Innovation Division
• The four divisions responsible for the supervision, as a host regulator, of the UK activities of international banks, custodians and designated investment firms headquartered in over 50 overseas jurisdictions – including some of the world’s highest profile firms.

Department Overview

This role is within the RegTech, Data and Innovation Division (RDID), which works to deliver against the PRA Strategic Review objective to make the most of data and technology, consistent with the wider Bank’s Strategic Priority 7 to modernise the Bank’s ways of working.

In September 2021, PRC agreed a multi-year programme to ‘deliver a step change in the PRA’s efficiency, effectiveness, and data culture by 2026, through phased investment in tools, technology, processes and skills.

Since then, RDID has been working hard with colleagues across the PRA and in the wider Bank to help ensure we are equipped to deliver against that ambition. In 2022, we laid the foundations and multi-year forward plan in the PRA 2026 strategic data roadmap, as well as rolling out early tools and platforms. In 2023, we entered delivery mode for the agreed priority projects within that roadmap.

RDID is made up of four teams:

• Data Governance Team
• Digital Transformation Team
• Operational Strategy & Delivery Team
• Technical Capabilities Team

Job Description

The Technical Capabilities team within RDID consists of a range of data professionals including data analysts, data engineers, data scientists and specialist data modellers.

As a data scientist within the Technical Capabilities Team, you will lead and assist the development of advanced analytics through deployment of data science and artificial intelligence/machine learning techniques to improve a variety of internal business functions related to financial supervision and regulation.

You will coordinate and feed into a number of key PRA data projects and will have autonomy in implementing the best solutions, including training end users and the development of new analytical tools. The successful candidate will be responsible for challenging the status quo and with fellow colleagues will aim to enhance analytical capabilities across the PRA. To achieve this, you will need to demonstrate strong command of relevant programming languages (Python, R, SQL), data visualisation tools (Tableau, Power BI, R Shiny, Streamlit), experience in using Cloud technologies and willingness to continuously improve your knowledge.

Being part of a team of experienced data professionals you will be contributing to regulatory, financial, exploratory and digital analysis. As part of the role, you will have the opportunity to feed into or lead some of the most important analytical pieces used by the PRA to ensure the safety and soundness of firms we supervise.

In addition, the post-holder will contribute to the PRA and Bank-wide agendas on improving data quality at firms, increasing digital skills and embedding AI/ML techniques throughout the PRA’s analytical suite. The role offers a fantastic opportunity to improve your understanding of finance and regulation.

There is also the potential to enrol in related courses of interest or embark on an advanced data science related qualifications, such as Bank-sponsored Apprenticeships and/or MSc in Data Science, or industry-recognised certifications (e.g. MS Azure).

Responsibilities

• Deliver analytical tools and outcomes that allow the PRA to work more efficiently – whether in their use of data, automation of regular tasks, or new and innovative ways to model and visualise data
• Produce advanced data analysis, models, reporting and new insights to support the wider business areas to identify and manage risks
• Collaborate and build relationships inclusively with end-users including supervisors of banks and insurers to understand their needs and ensure tools meet those needs
• Keep abreast of the latest developments in the field of data science, and share knowledge to a range of technical and non-technical audiences
• Help drive forward the growth, awareness and uptake of RegTech and SupTech tools and technologies (supervisory tech) across banking and insurance supervision. This may include presenting ideas or explaining new products/tools to both senior committees and frontline supervision; or contributing to PRA’s Research Agenda on SupTech/RegTech
• Attend technical events outside the PRA with industry stakeholders and other central banks / regulators

Role Requirements

As a minimum, a candidate should be able to demonstrate:

• Experience in a role that uses programming languages (Python/R) to manipulate data and draw insights from large quantitative and qualitative data sets
• Experience visualising/presenting data for stakeholders using Tableau or similar data visualisation tools
• Good analytical skills and ability to synthesise key messages from data, both in writing and verbally
• Proactive attitude – a drive to learn and master new technologies and techniques developing your data science knowledge
• Ability to organise own workload independently, or as part of a team, to meet business requirements on time
• Good inter-personal skills and experience of working in a team, including evidence of building effective stakeholder relationships, having personal impact and influencing abilities
• Good awareness of software engineering and coding best practices

One or more of the following would be desirable:

• Advanced skills in data modelling, ETL, process design and performance optimisation
• Practical experience with classical NLP techniques and text pre-processing
• Practical experience building solutions from transformers-based models for NLP problems, e.g. embeddings generation, information retrieval, entity recognition, Retrieval Augmented Generation
• Experience with Python natural language processing (NLP) and language modelling libraries (e.g. Transformers, Sentence Transformers)
• Hands-on experience with large foundation models and related frameworks, e.g. model fine-tuning, quantisation/low-rank compression, prompt engineering
• Experience with Microsoft Azure data scientist stack (e.g. Azure ML, Databricks, cognitive services, OpenAI)
• An interest in financial markets and regulation and keenness to learn about financial concepts and metrics