Data Scientist

The Alan Turing Institute
London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Data Scientist (NLP & LLM Specialist)

THE ALAN TURING INSTITUTE

Named in honour of Alan Turing, the Institute is a place for inspiring, exciting work and we need passionate, sharp, and innovative people who want to use their skills to contribute to our mission to make great leaps in data science and AI research to change the world for the better.

Please find more information about us here .

BACKGROUND


The Applied Research Centre (ARC) sits within The Alan Turing Institute s Defence & National Security (D&NS) programme, working with partners across UK Government to turn real-world data challenges into research questions and apply cutting-edge Deep Learning/AI in a readable, reliable and reproducible way.

CANDIDATE PROFILE


An experienced researcher/data scientist (PhD or equivalent experience) with strong programming and advanced statistical/numerical foundations, practical experience or clear interest in Deep Learning/AI, and confidence working in modern research languages (e.g. Python). You ll have excellent research software engineering habits (version control, testing, reproducibility), communicate clearly through technical writing and presentations, and be comfortable learning new domains fast. Eligibility for SC clearance is required.

DUTIES AND AREAS OF RESPONSIBILITY

  • Understand partner problems and shape appropriate approaches/experiments
  • Apply state-of-the-art Deep Learning/Data Science to D&NS challenges
  • Run rigorous investigations, producing deployable proof-of-concept code and technical write-ups
  • Design and execute experimentation in small teams (with guidance from senior staff)
  • Present and document work for reuse
  • Work at pace using standard tooling for testing, version control and collaboration; contribute to technical excellence

PERSON SPECIFICATION

  • PhD or equivalent professional experience using programming and advanced statistical/numerical methods
  • Interest/experience in Deep Learning/AI; fluency in a modern research language (e.g. Python)
  • Strong reproducible/RSE practices (VC, issue tracking, automated testing, experiment management)
  • Excellent written/verbal communication; stakeholder-oriented thinking
  • Team-working, independent planning/execution, problem-solving and data analysis/reporting skills
  • PyTorch/TensorFlow/Jax/Transformers; problem scoping with customers; managing research data/experiments; AI tooling awareness
  • Commitment to EDI and organisational values; eligible for SC clearance

Please see our portal for a full breakdown of the Job Description.

TERMS AND CONDITIONS

This full-time post is offered on a permanent basis. The annual salary is £45,505-£51,241 including flexible working and family friendly policies Employee-only benefits guide The Alan Turing Institute .

The Alan Turing Institute is based at the British Library, in the heart of London s Knowledge Quarter. We expect staff to come to our office at least 4 days per month. Some roles may require more days in the office; the hiring manager will be able to confirm this during the interview.

APPLICATION PROCEDURE

Please see our jobs portal for full details of how to apply and the application process.

EQUALITY DIVERSITY AND INCLUSION

We are committed to making sure our recruitment process is accessible and inclusive.

This includes making reasonable adjustments for candidates who have a disability or long-term condition. Please contact us at to find out how we can assist you.

Tracking.aspx?MsepY%2b%2fn9XKdrk9nNomVErw6CtFviwOTv

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.