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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 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 KTP Associate

Opportunity Overview This post is a dynamic role that will develop bespoke AI-driven tools to automate motor insurance claim analysis, enhancing efficiency and accuracy. The KTP will embed advanced machine learning capabilities, reduce case turnaround times, and unlock scalable growth, transforming data handling in the UK's high-volume, fraud-prone motor insurance sector. The position will be based at the company premises...

University of Salford
Salford

Data Scientist

Description:- We are seeking a highly skilled Data Scientist AI to design, develop, and deploy advanced machine learning and artificial intelligence solutions. The ideal candidate will work on large datasets, build predictive models, and collaborate cross-functionally to deliver scalable, data-driven products. Key Responsibilities:- Design, develop, and optimize machine learning and deep learning models.Work on AI/ML projects including NLP, computer vision,...

InfinityQuest,
Uxbridge

Data Scientist - Inside IR35 - Optimisation

Data Scientist - Optimisation & Applied Analytics Contract duration: 6months (12months of work road mapped) Day rate: 800 Inside IR35 Location/Hybrid: 2-3 days per week to Heathrow Airport A major global airline, undergoing a multi-million-pound data and optimisation transformation, is looking for a Data Scientist to join their high-performing Decision Support & Optimisation function. This role is end-to-end, hands-on, and...

Lorien Resourcing
West Drayton

Data Scientist

The Role : Data Scientist : Graph database and ontology specialist We are seeking a Data Scientist with deep expertise in Knowledge Graphs and Ontologies and the ability to work across domains. You will design and deploy production-grade graph solutions that model relationships not only between UAVs, missions, and sensors, but across company processes end-to-end: from operations and production to...

TEKEVER
Bristol
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Career Advice

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

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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