Data Scientist

COMPETITION & MARKETS AUTHORITY
Cardiff
2 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (eDV clearance required)

Data Scientist

Ref:

384312Salary:

£46,000 - £49,950A DDAT allowance of up to £4750 may be applicableA Civil Service Pension with an employer contribution of 28.97%Location:

Belfast, Cardiff, Edinburgh, London, ManchesterClosing date:

11:55 pm on Monday 13 January 2025.

Job summaryThis is an exciting opportunity to use your data science skills for the public good and join the Competition and Markets Authority (CMA) as a data scientist within its Data, Technology and Insights Directorate. The directorate is a multidisciplinary team that brings together data scientists, data engineers, technologists, behavioural scientists and digital forensics and eDiscovery experts. It provides the CMA with specialist skills and capabilities to keep pace with fast-moving digital markets, rapidly developing business models and the growing use of data and algorithms.The CMA is a world-leading competition authority at the forefront of tackling pressing public policy questions, including the regulation of the biggest digital markets. Our mission is to make markets work well in the interests of consumers, businesses, and the economy.

The data science and engineering teamYou will join an established team of approximately 20 data scientists and data engineers within the wider DaTA Unit. We provide analytical support for CMA legal cases and develop tools to improve how the CMA operates. We build most of our tools ourselves, on top of open-source software, using our custom analysis platform built in AWS. Data scientists in our team have space to explore the use of new, cutting-edge techniques in data processing and machine learning, including large language models and generative AI. We work in a collaborative way, with a focus on sharing our knowledge and experience across the team.Examples of projects we’ve worked on recently include:Building a machine learning tool that collects and classifies news articles to support the CMA’s merger investigation teamExperimenting with and testing LLMs as part of our digital transformation strategyUsing web-scraped data to run a causal study to determine the features in a tech platform’s ranking algorithm.

Job descriptionAs a Data Scientist, you will work with senior team members on the technical delivery of data science and analysis projects to support CMA legal cases. You will contribute to scoping projects and designing their data science solutions and assist in identifying competition and consumer protection issues that the CMA could address using data science methodsYou will develop data science tools to support the functions of the CMA, produce "production" code in Python (other languages such as R or SQL might be used as needed) and assist other team members with regular code reviews.Working within a multi-disciplinary case team, you will write and present the output of analyses to both technical and non-technical stakeholders and develop strong working relationships across the CMA.

Person specificationIt is essential that you can provide evidence and examples for each of the following selection criteria in your application.Experience delivering data science projects as part of a team and experience of shaping and leading analytical projects (Lead Criteria)Strong knowledge of modern machine learning methods and tools, as well as data extraction and manipulation (knowledge of data engineering tools and techniques, such as cloud services and git version control is a plus) (Lead Criteria)A degree in a quantitative subject or equivalent experience working in data scienceSubstantial ability and experience of coding in Python and/or RGood communication skills, with the ability to write reports for a non-technical audience and to present analysis in a straightforward and engaging wayExperience of working collaboratively with stakeholders, building excellent working relationships.While a good understanding of competition and consumer issues would be useful, this is not essential, and the CMA prides itself on ensuring that staff can grow and develop their skills.We recruit by merit on the basis of fair and open competition, as outlined in the Civil Service Commission's recruitment principles.The Civil Service embraces diversity and promotes equal opportunities. As such, we run a Disability Confident Scheme (DCS) for candidates with disabilities who meet the minimum selection criteria.The Civil Service also offers a Redeployment Interview Scheme to civil servants who are at risk of redundancy, and who meet the minimum requirements for the advertised vacancy.The Civil Service is committed to attract, retain and invest in talent wherever it is found.

Contact point for applicantsJob contact: Tom Skidmore,

team,

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.