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Location(s): UK, Europe & Africa : UK : Frimley
BAE Systems Digital Intelligence is home to 4,500 digital, cyber, and intelligence experts. We work collaboratively across 10 countries to collect, connect, and understand complex data, so that governments, nation states, armed forces, and commercial businesses can unlock digital advantage in the most demanding environments.
Location: South of England - 4-5 days per week based on client site.
About the Role
We are looking for a Data Scientist to join our Digital Defence Services team following continuous growth and success. Within Digital Defence Services, we are a critical partner to the UK Ministry of Defence in their adoption of secure digital solutions that enable multi-domain integration and data exploitation, which provides the advantage to those who serve and protect us. Positioned within a thriving Digital Defence Services Business Unit and part of a wider vibrant Security Consulting Community from across other sectors, you will be supported in the role to learn and develop, with clear pathways defined for your career progression in the organisation.
Our people are what differentiates us; they are resourceful, innovative, and dedicated. We have a mix of generalists and specialists and recognise that this diversity contributes to our success. We recognise the benefits of forming teams from a mix of disciplines, which allows us to come up with cutting-edge, high-quality solutions. Our breadth of work across the public sector provides diverse opportunities for our people to develop their careers in new areas and with new clients.
Core Duties
- Design, develop, and test solutions to collect, integrate, and prepare data for advanced analytics and machine learning applications.
- Analyse complex datasets to uncover trends, patterns, and actionable insights that drive business or operational outcomes.
- Build, prototype, and evaluate statistical and machine learning models to solve real-world problems, testing feasibility and estimating impact before full deployment.
- Engineer and implement ML-based solutions, owning the full lifecycle – from model development and deployment to monitoring and iteration.
- Deploy models into production environments, handling the integration and operationalisation of ML within wider systems and applications.
- Continuously evaluate and monitor model performance, identifying degradation, performance gaps, or opportunities for optimisation.
- Collaborate closely with data analysts, engineers, and other stakeholders to define new tools, enhance workflows, and support innovation across teams.
- Communicate findings, recommendations, and model outcomes to both technical and non-technical audiences through visualisation and data storytelling.
- Research emerging AI/ML techniques to stay ahead of the curve and identify new opportunities to enhance current systems.
- Ensure all data science and ML practices adhere to relevant ethical standards, policies, and governance frameworks.
- Provide technical guidance and mentorship on ML implementation across cross-functional teams.
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Data Science and Analytics
- Use and design of algorithms is expected from the data scientist, to extract meaningful, actionable insight from a variety of datasets. The data scientist should take the initiative to develop, test, and deploy tooling across a range of technologies including but not limited to (1) Elastic, Logstash, Kibana (ELK) and its equivalents (2) Ni-Fi (3) Python (4) Geospatial intelligence software (5) APIs from commercial/open-source providers.
- The data scientist will be expected to conduct exploratory analysis of datasets to address a range of client problem sets.
Open-Source Intelligence and Data Exploitation
- The data scientist is not expected to be trained/experienced in Open-Source Intelligence; however, their role will include working with a range of datasets in support of this objective. The data scientist should apply a range of techniques and exploitation to lead to improves customer outcomes and highlight drawbacks/shortcomings of datasets in a timely manner.
- As part of their professional development, it is beneficial to have a data scientist that will take the initiative and attend training which will improve their tradecraft, techniques, and investigative methods
Key Requirements
- You have a strong foundation in data science, analytics, or machine learning, with hands-on experience developing models that solve practical problems and deliver measurable impact.
- You are comfortable working across the full machine learning lifecycle – from exploratory data analysis and model prototyping to production deployment, integration, and ongoing monitoring.
- You are proficient in Python and its data/ML ecosystem (e.g. pandas, scikit-learn, PyTorch, TensorFlow), and you can apply statistical and machine learning techniques confidently in real-world settings.
- You have deployed models into live systems and understand how to make ML operational – whether that means working with APIs, integrating into existing applications, or using containerisation tools like Docker.
- You actively monitor the performance of deployed models, and are experienced in identifying drift, re-training triggers, or opportunities for optimisation.
- You stay current with the latest advancements in machine learning and AI and enjoy applying new methods or tools to improve systems and outcomes.
- You are aware of the ethical and governance considerations that come with deploying machine learning at scale – such as bias, fairness, explainability, and compliance – and you incorporate these into your work.
- You are a strong communicator who can translate complex technical work into clear insights and recommendations, adapting your message to suit both technical and non-technical stakeholders.
- You enjoy working in cross-functional teams, contributing your expertise while collaborating with analysts, engineers, product teams, and decision-makers.
- You are self-motivated, solution-oriented, and take ownership of your work – from scoping a problem through to delivering a production-ready solution.
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Life at BAE Systems Digital Intelligence
We are embracing Hybrid Working. This means you and your colleagues may be working in different locations, such as from home, another BAE Systems office, or client site, some or all of the time, and work might be going on at different times of the day.
By embracing technology, we can interact, collaborate, and create together, even when we’re working remotely from one another. Hybrid Working allows for increased flexibility in when and where we work, helping us to balance our work and personal life more effectively, and enhance well-being.
Diversity and inclusion are integral to the success of BAE Systems Digital Intelligence. We are proud to have an organisational culture where employees with varying perspectives, skills, life experiences, and backgrounds – the best and brightest minds – can work together to achieve excellence and realise individual and organisational potential.
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