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Featured Jobs
Data Scientist
Dta Scientist, AI, Python, LLM, React, UK, Remote to £80,000 A growing Data Analytics outfit are looking for a Data Scientist as they look to build robust platforms to deliver AI Data insights. They are looking for a Data Scientist who has experience with data heavy products, data science, AI and statistical modelling. You will also have some full stack...
Ascend Consulting
London
Data Scientist
Data Scientist: Country Risk & Advanced Analytics Join an integrated team of economists, political scientists, and computer scientists to shape the strategic decisions of the world's leading organizations. How You'll Make an Impact: Innovate: Prototype new approaches for extracting insights from structured and unstructured data. Build: Design and optimize risk models for analytics and generative AI applications using proprietary NLP...
Randstad Technologies Recruitment
London
Data Scientist
Keystone Recruitment Partners are delighted to be working exclusively for our International Data Science Consultancy client working with some of the world's most recognisable brand companies providing creative and elegant data science solutions to real-world supply chain, manufacturing and logistics problems. Our client pride themselves on being at the forefront of AI, Data, Software and Technology providing their clients with...
Keystone Recruitment Partners Ltd
London
Data Scientist
Are you ready to turn data into action and make a tangible impact? We’re looking for a skilled and driven Revenue & Debt Data Scientist to join a dynamic Credit Risk function. This is a unique opportunity to work at the forefront of credit risk analytics, helping shape smarter collections strategies, reduce bad debt, and improve outcomes for customers. In...
Cactus Search
Gloucester
Data Scientist
We are currently recruiting for a Contract Data Scientist to support an innovative engineering-focused organisation operating within a fast-paced R&D environment. This is an excellent opportunity for a hands-on data specialist with strong MATLAB expertise who enjoys working closely with engineering teams to drive product development through high-quality data analysis. The Role As a Contract Data Scientist, you will play...
SoCode Limited
Cambridge
Data Scientist
Our client is looking for an experienced Data Scientist to design, build, and optimise machine learning models and advanced analytics solutions that support institutional priorities across a large, complex network. The role blends hands-on data science with strategic impact, using AWS technologies to deliver predictive insights that drive proactive interventions and data-driven decision-making. This is a hybrid role with the...
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.
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.
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.
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.
Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action.
Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work.
In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert.
Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent.
This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.
If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day.
Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making.
This guide focuses on the only maths most data scientists keep using:
Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty)
Probability for real-world data (base rates, noise, sampling, Bayesian intuition)
Linear algebra essentials (vectors, matrices, projections, PCA intuition)
Calculus & gradients (enough to understand optimisation & backprop)
Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds)
You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.
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