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Featured Jobs
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...
Spectrum IT Recruitment
London
Data Scientist - Imaging - Remote - Outside IR35
This is a fantastic opportunity to work as a Data Scientist, working with Scientists, for a major pharmaceutical company, on a remote basis, outside IR35. The key skills required for this Data Scientist vacancy are: Extract knowledge and insights from image-based data Work with biomarker scientists and pathologists to interpret analysis results Data preparation, modelling, analysis and visualization If you...
The Bridge IT Recruitment
Elephant & Castle
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 Scientist
Job Description Join the DataAnnotation team and contribute to developing cutting-edge AI systems, while enjoying the flexibility of remote work and setting your own schedule.We are looking for an expert Mathematician (part-time work from home) to help advance AI development. As a member of DataAnnotation’s Math team, you’ll be part of a growing community of over 100,000 experts who are...
DataAnnotation
Coventry
Data Scientist
Job Description We work with political parties, investors, media organisations, think tanks, NGOs and companies of all sizes around the world to help them understand public opinion, how it affects them, and what they should do in response to it. We have developed the most accurate models for predicting election outcomes that exist in the market, and have deployed them...
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.
Data science is all about turning messy, real-world information into decisions, products & insights. It sits at the crossroads of maths, coding, business & communication – which means it needs people who see patterns, ask unusual questions & challenge assumptions.
That makes data science a natural fit for many neurodivergent people, including those with ADHD, autism & dyslexia.
If you’re neurodivergent & thinking about a data science career, you might have heard comments like “you’re too distracted for complex analysis”, “too literal for stakeholder work” or “too disorganised for large projects”. In reality, the same traits that can make traditional environments difficult often line up beautifully with data science work.
This guide is written for data science job seekers in the UK. We’ll explore:
What neurodiversity means in a data science context
How ADHD, autism & dyslexia strengths map to common data science roles
Practical workplace adjustments you can request under UK law
How to talk about your neurodivergence in applications & interviews
By the end, you’ll have a clearer sense of where you might thrive in data science – & how to turn “different thinking” into a real career advantage.
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