Senior Data Scientist – Machine Learning -  Defence –Eligible for SC

Paddington
9 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist - ML Solutions & Flexible Work

Senior Data Scientist: Build Production ML Solutions

Senior Data Scientist: Greenfield ML + Abu Dhabi Relocation

Senior Data Scientist - Advanced Analytics (Hybrid)

Senior Data Scientist - Hybrid Remote for Supply Chain

Senior Data Scientist – US Credit Risk ML, Remote M/W F

Senior Data Scientist – Machine Learning - Defence – Hybrid – Eligible for SC

I’m working with a leading Tech company that provide Machine Learning and Data Science expertise to the Defence sector, developing advanced analytics and AI solutions. They collect their own data for building and deploying cutting-edge Machine Learning technologies that enhance national security. They are in the process of building a new Data Science / AI team from the ground up and you'll have the opportunity to shape the technical direction and capabilities of their products. This will be a truly greenfield role where you get to build something from scratch. Experience of doing this previously will be highly valuable as will experience in the Defence sector.

The Role

As the successful Senior Data Scientist, you'll be part of a small team developing AI solutions for defence applications, working with unique datasets . This position offers the chance to apply machine learning in an environment with direct impact on real-world operations.

They provide intelligence data to support Defence operations so we need someone who is ethically aligned with their business mission.

Responsibilities

  • Apply data science techniques to defence challenges including Object Detection, Track Fusion, Graph Data / Clustering, Reinforcement Learning, and LLM/RAG deployment

  • Conduct ML research and lead operational deployment to build models from scratch

  • Mentor junior team members

  • Implement Data Science best practices

  • Stay current with emerging trends in AI, sensor technologies, and simulation techniques

    Requirements

  • PhD or an MSc in a quantitative field (Computer Science, Physics, Mathematics, or related STEM discipline)

  • AT least 3-4 years Data Science experience and proven skills in a Senior Data Scientist role.

  • Strong experience of at least one of the following and appreciation of the rest: Object Detection, Track Fusion, Graph Data / Clustering, Reinforcement Learning, and LLM/RAG deployment

  • Strong Python programming skills and proficiency with ML libraries

  • Familiarity with MLOps and Cloud platforms (Azure preferred – Azure Cloud Platform, Azure DevOps, Azure AI tools)

  • Security clearance (SC) essential or the ability to obtain it

  • Clear communication skills for technical and non-technical audiences

    What Sets Them Apart

  • Direct Data Access: They collect their own data, eliminating the data silo issues common in defence work

  • End-to-End Development: Build solutions from scratch and see them deployed in the real world

  • Practical Impact: Contribute to projects that enhance national security

  • Balanced Approach: Initially 80% operational work and 20% research, shifting toward more research as capabilities mature

  • Innovation Potential: Opportunity to develop ideas that could become standalone products

    Work Arrangement

  • Hybrid working (minimum one day per week in office)

  • Office locations in London, Oxford or Lincolnshire

  • Salary: £80,000 (based on experience) + bonus + benefits

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.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

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.

How to Write a Data Science Job Ad That Attracts the Right People

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.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

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.