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Featured Jobs
Data Scientist Placement
Are you in your penultimate year of study and looking to work in a fast paced, global, market leading company for your industrial placement? Here at Innovative Technology we have an excellent opportunity for a Data Science Intern to join our talented team in our global head office in Oldham, Greater Manchester for 12 months starting in Summer 2026. The...
Innovative Technology
Oldham
Data Scientist (NLP & LLM Specialist)
Data Scientist (NLP & LLM Specialist) Remote- UK 6-month contract with Potential Extension Day rate - £427.68 - £565 per day Inside IR35 Data Scientist (NLP & LLM Specialist) Are you an expert in Natural Language Processing who thrives on building scalable, real-world AI solutions? We are seeking a hands-on Data Scientist to join a premier global credit ratings and...
Randstad Technologies Recruitment
London
Data Scientist (Predictive Modelling) – NHS
Data Scientist (Predictive Modelling) – NHS SR2 Consulting has an urgent requirement for a Data Scientist to support an NHS client on a contract basis. Our client is delivering a data-led solution to support discharge planning and patient flow in an acute healthcare setting. We are seeking a data scientist with experience in predictive modelling, clinical data, and EPR systems...
SR2
Farringdon, Greater London
Data Scientist - Measurement Specialist
Our client,an award winning SaaS organisation providing software solutions to the SME marketplace, is now seeking an experienced Data Scientist for a 12 month contract. You will be assisting in the company transition from correlation-based reporting to causal-based decision making, helping guide key marketing investment decisions. Central London location, hybrid, with 3 days a week in the office. Responsibilities Forecasting:...
EF Recruitment
Victoria, Greater London
Data Science Placement Programme
Data Scientist Placement Programme - No Experience Required Our training will help you kick-start a new career as a Data Scientist. We are recruiting for companies who are looking to employ our Data Science Traineeship graduates to keep up with their growth. The best part is you will not need any previous experience as full training will be provided. You...
Career Change
The Royal Town of Sutton Coldfield
Data Science Placement Programme
Data Scientist Placement Programme - No Experience Required Our training will help you kick-start a new career as a Data Scientist. We are recruiting for companies who are looking to employ our Data Science Traineeship graduates to keep up with their growth. The best part is you will not need any previous experience as full training will be provided. You...
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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