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AI Engineer - Databricks and Spark essentialLondon based.Start in May until 31st Dec.Certain Advantage are recruiting for an AI Engineer with hands on Databricks and Spark expertise. Industry experience in commodity or financial trading is...
Tatton Recruitment
South Bank, London, SE1 9PZ, United Kingdom
Principal Data ConsultantA dynamic, leading boutique data strategy consultancy is seeking a Principal Data Consultant for an immediate start.This is a fantastic opportunity to work on interesting and complex data strategy and transformation projects. You...
Fynity
Dowgate, City And County Of the City Of London, United Kingdom
Data & Insights Manager£60,000 – £65,000Hybrid - Bridgwater, Somerset 3 days/weekThis is a rare opportunity to step into a leadership role at the intersection of data strategy and real-world impact. You’ll shape how an organisation...
Vocative Consulting
Bridgwater, Somerset, United Kingdom
Hiring: ERP Data Solutions Architect (Contract/Permanent) – Dublin, Ireland 🌍Are you an expert in SAP data architecture and enterprise integrations? We’re looking for a ERP Data Solutions Architect to lead critical initiatives in SAP Cloud...
Dabster Systems UK Limited
Dublin, City Of Dublin, Ireland
Isomorphic Labs is applying frontier AI to help unlock deeper scientific insights, faster breakthroughs, and life-changing medicines with an ambition to solve all disease.The future is coming. A future enabled and enriched by the incredible...
Isomorphic Labs
London, United Kingdom
Principal Data Analyst (SC Cleared - Aerospace/Defence)📍 Location: Reading (Hybrid - 2-3 days onsite, flexibility available)🔐 Clearance: SC Cleared (Active)💼 Contract Type: Inside IR35💰 Rate: £80 - £83 per hourOverviewAre you an experienced SC Cleared...
CBSbutler Holdings Limited trading as CBSbutler
Reading, Berkshire, United Kingdom
Advance your Data career with expert advice, practical job search tips, and insightful industry guides.
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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.
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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.
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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.
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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.
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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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Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.
