Senior Data Science and Machine Learning Researcher

Searchability NS&D
Manchester
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist and Machine Learning Researcher

Data Scientist, United Kingdom - BCG X

Data Scientist, United Kingdom - BCG X

Data Scientist, United Kingdom - BCG X

Data Scientist, United Kingdom - BCG X

Data Scientist, United Kingdom - BCG X

Senior Data Science and Machine Learning Researcher

Be among the first 25 applicants.


This range is provided by Searchability NS&D. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.


Base pay range

Direct message the job poster from Searchability NS&D



  • Up to £65k DoE plus package
  • Manchester location – circa 3 days on site
  • Active SC and eDV eligibility required
  • High‑impact R&D role with strong funding and long‑term growth

About the client

Our client is a highly specialised technology organisation operating in a secure, mission‑focused environment within the National Security sector. Working as part of a small, well‑funded research group within a growing area of the business, this team delivers innovative data science and machine learning solutions to complex customer problems.


The benefits

  • Tiered clearance bonus
  • Funded R&D projects and internal seed investment
  • Clear technical growth and progression opportunities
  • Supportive, collaborative team environment

The Senior Data Science and Machine Learning Researcher role

As a Senior Data Science & Machine Learning Researcher, you will focus on research‑led development, working across short exploratory tasks and longer‑term R&D initiatives. You will help shape project direction, translate customer needs into technical solutions, and build innovative models and approaches that can be taken forward into delivery. This role suits someone comfortable working at a higher level of ambiguity, with the freedom to define what should be worked on next.


Essential skills

  • Strong background in data science and machine learning research
  • Experience developing and prototyping novel algorithms or approaches
  • Ability to take research concepts through to practical application
  • Confidence engaging with stakeholders to understand customer needs
  • Active SC clearance and eDV eligibility

To be considered

Please either apply through this advert or email me directly via . For further information, please call me on . By applying for this role, you give express consent for us to process and submit (subject to required skills) your application to our client in conjunction with this vacancy only.


Key skills

Data Science, Machine Learning, Research and Development, Algorithms, Python, Stakeholder Engagement


Job details

  • Seniority level: Mid‑Senior level
  • Employment type: Full‑time
  • Job function: Information Technology, Consulting, and Science
  • Industries: Research Services, IT Services and IT Consulting, and IT System Data Services


#J-18808-Ljbffr

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.

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.

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.

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.