Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Science

London
5 days ago
Create job alert

Experis

London - Hybrid

Employment Type - Full time

Department - Defence

What you'll be doing:

As a Data Scientist in our Defence business unit you will be part of project teams that deliver bespoke algorithms to our clients across the Defence sector. You will be responsible for conceiving the data science approach, for designing the associated software architecture, and for ensuring that best practices are followed throughout.

You will help our excellent commercial team build strong relationships with clients, shaping the direction of both current and future projects. Particularly in the initial stages of commercial engagements, you will guide the process of defining the scope of projects to come with an emphasis on technical feasibility. We consider this work as fundamental towards ensuring that the company can continue to deliver high-quality software within the allocated timeframes.

The company has earned wide recognition as a leader in practical data science. You will actively contribute to the growth of this reputation by delivering courses to high-value clients, by talking at major conferences, by participating in external roundtables, or by contributing to large-scale open-source projects. You will also have the opportunity to teach on the fellowship about topics that range from basic statistics to reinforcement learning, and to mentor the fellows through their 6-week project.

Thanks to the company platform, you will have access to powerful computational resources, and you will enjoy the comforts of fast configuration, secure collaboration and easy deployment. Because your work in data science will inform the development of our AI products, you will often collaborate with software engineers and designers from our dedicated product team.

Who we're looking for:

Proven experience in either a professional data science position or a quantitative academic field
Strong programming skills as evidenced by earlier work in data science or software engineering. Although your programming language of choice (e.g. R, MATLAB or C) is not important, we do require the ability to become a fluent Python programmer in a short timeframe
An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe)
A high level of mathematical competence and proficiency in statistics
A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model cross validation, Bayesian inference, time-series analysis, simple NLP, effective SQL database querying, or using/writing simple APIs for models. We regard the ability to develop new algorithms when an innovative solution is needed as a fundamental skill
An appreciation for the scientific method as applied to the commercial world; a talent for converting business problems into a mathematical framework; resourcefulness in overcoming difficulties through creativity and commitment; a rigorous mindset in evaluating the performance and impact of models upon deployment
Some commercial experience, particularly if this involved client-facing work or project management; eagerness to work alongside our clients; business awareness and an ability to gauge the commercial value of projects; outstanding written and verbal communication skills; persuasiveness when presenting to a large or important audience
Experience leading a team of data scientists (to deliver innovative work according to a strict timeline) as well as experience in composing a project plan, in assessing its technical feasibility, and in estimating the time to deliveryWhat we can offer you:

The company team is diverse and distinctive, and we all come from different personal, professional and organisational backgrounds. We all have one thing in common: we are driven by a deep intellectual curiosity that powers us forward each day.

The company is the professional challenge of a lifetime. You'll be surrounded by an impressive group of brilliant minds working to achieve our collective goals.

Our consultants, product developers, business development specialists, operations professionals and more all bring something unique to the company, and you'll learn something new from everyone you meet.

People Source Consulting Ltd is acting as an Employment Agency in relation to this vacancy. People Source specialise in technology recruitment across niche markets including Information Technology, Digital TV, Digital Marketing, Project and Programme Management, SAP, Digital and Consumer Electronics, Air Traffic Management, Management Consultancy, Business Intelligence, Manufacturing, Telecoms, Public Sector, Healthcare, Finance and Oil & Gas

Related Jobs

View all jobs

Data Engineer

Senior Data Scientist

Sr AWS Data Engineer

Junior Data Analyst

Junior Data Analyst

Junior Data Analyst

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 Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.