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Featured Jobs

£41,574 – £44,535 pa On-site Contract Part-time

Performance Data Engineer/Scientist

Department and salaryDepartment - Sport System Institute ServicesSalary - £41,574.01 – £44,535.11 (pro rata)Working Hours - 4 days (30 hours)Type of Contract - 1 year fixed termLocation - Sport Wales National Centre, CardiffWhat you’ll needWe...

Sports Wales

Sports Wales

Cardiff, United Kingdom

£80,000 – £120,000 pa On-site Permanent

Senior Operating Partner, Drug Design, London

As a Senior Operating Partner in Drug Design, you will work closely with the Chief Scientific Officer and Chief Research Officer to drive high-impact initiatives and operationalize key strategies. Your role involves synthesizing outputs, problem-solving across functions, and ensuring alignment with leadership goals, all while fostering a collaborative and agile team environment.

Isomorphic Labs

Isomorphic Labs

London, United Kingdom

£50,000 – £70,000 pa Hybrid Permanent

UK Lead: Real Estate Lease Admin & Data

This role involves overseeing the administration and governance of a large commercial real estate lease portfolio, ensuring data accuracy and strategic control. Responsibilities include managing lease data, supporting audits, and leading a team to develop reporting tools for real-time decision-making. The position requires strong data governance, stakeholder management, and experience in commercial lease administration.

Michael Page

London, United Kingdom

£30,000 – £45,000 pa Hybrid Permanent

Junior Data Scientist

This role involves working on applied machine learning and data analysis, contributing to the entire model lifecycle from exploration to deployment. You'll collaborate with scientists and engineers on complex datasets, focusing on robustness and practical impact in a mission-driven, growing R&D team.

Experis

Experis

Glasgow, City Of Glasgow, G2 1AL, United Kingdom

£40,000 – £50,000 pa Hybrid Contract

Senior Data analyst

The Senior Data Analyst role involves gathering, cleaning, and transforming data from various sources to provide meaningful insights. You will work closely with business teams and data engineers to ensure data accuracy and create visual reports using tools like Power BI and Tableau. The role emphasizes data governance, predictive modeling, and effective communication of insights.

Pontoon

Bromley, London, United Kingdom

£60,000 – £70,000 pa On-site Permanent

Lead Developer

The Lead Developer will shape the technical direction, architecture, and AI strategy for a next-generation operational platform supporting large-scale solar and energy assets. Key responsibilities include defining the platform architecture, setting the technical roadmap, leading technical delivery, and driving AI adoption across the platform. The role involves working closely with offshore development teams and ensuring strong governance in documentation, security, and compliance.

Astute People

Milton Keynes, Buckinghamshire, United Kingdom

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Career Advice

Advance your Data career with expert advice, practical job search tips, and insightful industry guides.

Products

Where to Advertise Data Science Jobs in the UK (2026 Guide)

Advertising data science jobs in the UK requires a different approach to most technical hiring. Data science spans a broad and often misunderstood spectrum — from statistical modelling and experimental design through to machine learning engineering, product analytics and AI research. The strongest candidates identify firmly with specific subdisciplines and are frustrated by adverts that conflate data scientist with data analyst, business intelligence developer or machine learning engineer. General job boards produce high application volumes for data roles but consistently fail to match specialist data science profiles with the right opportunities. This guide, published by DataScienceJobs.co.uk, covers where to advertise data science roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

Jobs

Data Science Jobs UK 2026: What to Expect Over the Next 3 Years

Data science has spent the past decade being described as the sexiest job of the twenty-first century. By 2026, the reality is both more nuanced and more interesting than that label ever suggested. The discipline has matured, fragmented, deepened, and in some respects reinvented itself — and the jobs market has changed with it in ways that create genuine opportunity for those who understand what employers actually want, and genuine difficulty for those still operating on assumptions formed five years ago. The data science jobs market of 2026 is not simply a larger version of what it was three years ago. The generalist data scientist — equally comfortable wrangling data, building models, and presenting insights to the board — is giving way to a more specialised landscape where employers know exactly what problem they are trying to solve and are looking for candidates with the specific depth to solve it. Machine learning engineering, causal inference, experimentation, AI product development, and domain-specific applied science have all emerged as distinct career tracks within what was previously a single, loosely defined profession. At the same time, the arrival of large language models and the broader AI capability wave has both threatened and created data science roles in equal measure. Some of the work that junior data scientists spent their early careers doing — data cleaning, exploratory analysis, basic model building — is being partially automated by AI tooling. But the demand for practitioners who can evaluate AI systems rigorously, apply statistical thinking to complex business problems, and build the data foundations on which AI depends has grown considerably. The candidates who will thrive over the next three years are those who understand where the discipline is heading — which specialisms are attracting the most investment, which technologies are reshaping what data scientists are expected to build and know, and how to position a data science career that will remain valuable as the field continues to evolve around them. This article breaks down what the UK data science jobs market is likely to look like through to 2028 — covering the titles emerging right now, the technologies driving employer demand, the skills that will matter most, and how to position your career ahead of the curve.

Jobs

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

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