Senior Data Scientist

Advanced Resource Managers
Birmingham
11 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist (GenAI)

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Permanent role details:

  • Senior/Data Scientist - Defence / MOD / Water Sectors
  • Location:Birmingham (Defence) / Leeds, Manchester, Liverpool (Water)
  • Basic Salary: £40,000 > £70,000 - Depending on experience.
  • Benefits:Life assurance, well-being, future development support, international remote working & more.
  • Must meet appropriate security clearance requirements, if suitable for this role.



The Energy/Defence DivisionatARMare currently recruiting an experienced Data Scientist to join one of the UK’s leading Design & Engineering Consultancies, to support their Defence, Aerospace, Security and Technology division on exciting upcoming projects across the UK.



Job Overview:

Our client specialises in delivering exciting data-led products and services to the largest public and private sector organisations in the UK, particularly in defence, security, intelligence, and government markets, as well as key clients in critical national infrastructure (CNI) across the world.

The Data & Artificial Intelligence team is a Centre of Excellence for Data Science, Data Engineering, Analytics, and Artificial Intelligence in the Aerospace, Defence, Security and Technology business of the world-leading Design, Engineering and Project-Management.



Some of your duties will include:

  • As a Data Scientist, you will be working collaboratively with experts across industry to uncover hidden insights and build AI solutions that solve important and impactful real-world problems within a growing team of data specialists.
  • You will leverage cutting-edge technology to analyse complex datasets and craft innovative solutions to a wide array of industry challenges.
  • Your continued growth and development goals will be met through a variety of projects that challenge you to stay up to date with the latest technological advances and embrace the challenges that consulting brings.
  • We will support you through our Learning Pathway and flexibility to try roles outside of your current areas of expertise.



What do you need to succeed?

  • Strong experience of programming languages like Python, R, and SQL to ingest and analyse datasets.
  • Knowledge and understanding of core Data Science, Machine Learning, and AI principles, considerations, and applications.
  • Understanding of how to build data products and services, including project/product management, prototyping, and deployment lifecycles.
  • An interest in helping junior team members to successfully deliver their projects.
  • Professional experience in Data Science.
  • Minimum 2.2 bachelor’s or above in Data Science, Computer Science, Mathematics, Statistics, Physics, Operational Research, Engineering, or related degree.



How to apply:

If you are interested to find out more about this opportunity, please apply via the link or contact Jasmine White at ARM Recruitment, and we will let you know if you have been shortlisted.

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.