Lead Data Scientist

Competition and Markets Authority
London
3 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist


Reference number: 434988

Salary: £61,575 - £69,238

Hours: Full time, part time, flexible working

Contract: Permanent

Location: Belfast, Cardiff, Edinburgh, London, Manchester

If you are an experienced Data Scientist with strong technical skills, then this is an exciting opportunity to play a key role in the delivery of data science projects at the Competition and Markets Authority (CMA).

About the CMA

We help people, businesses, and the UK economy by promoting competitive markets and tackling unfair behaviour. Our work is wide ranging, ambitious and often new and challenging.

The Data, Technology and Insight Unit (DTI Directorate) encompasses Strategic Insight, Analysis & AI and Technology & Digital. The Directorate provides the CMA with specialist skills and capability to keep pace with fast-moving digital markets, rapidly developing business models and the growing use of data and algorithms.

We ensure everyone in the CMA has access to the tools they require to effectively do their jobs and are leading our digital transformation programme to increase the organisations efficiency, effectiveness and capabilities including using AI. The Directorate is key to ensuring that the CMA can better access and analyse data (including large-scale or novel data with advanced tools & techniques) and understand firms' increasingly sophisticated use of AI and other emerging...

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

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

Industry Insights

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

Careers

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

Jobs

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

Education

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.