Data Scientist

BAE Systems Digital Intelligence; Frimley; On-site; GBP 40,000 - 70,000; Full time

Job summary

BAE Systems Digital Intelligence is looking for a Data Scientist to join their Digital Defence Services team in Frimley, UK. In this role, you will drive data-driven decision making across multiple domains, leveraging machine learning to enhance operations and solutions for the UK Ministry of Defence.

The ideal candidate will possess strong skills in Python and analytics, capable of translating complex data into clear recommendations. A UK National with a MoD/Partner DV clearance is required for this position.

Qualifications

• Hands-on experience in delivering machine learning models with measurable impact.
• Comfortable across the full machine learning lifecycle from prototyping to deployment.
• Awareness of ethical considerations in machine learning practices.

Responsibilities

• Design, develop, and test solutions for data analytics and machine learning.
• Analyse datasets to uncover trends and actionable insights.
• Collaborate with engineers and analysts to define new tools and enhance workflows.

Skills

Python proficiency

Data analysis

Machine learning

Data storytelling

Statistical modeling

Education

Background in data science or analytics

Tools

pandas

scikit-learn

PyTorch

TensorFlow

Docker

Elastic stack (ELK)

Job description

Location

UK, Europe & Africa : UK : Frimley

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 adopting secure digital solutions that enable multi-domain integration and data exploitation. Positioned within a thriving Digital Defence Services Business Unit and part of a wider vibrant Security Consulting Community, you will be supported to learn, develop and progress within the organisation.

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.

Data Science and Analytics

• Use and design of algorithms to extract meaningful, actionable insight from a variety of datasets. Develop, test, and deploy tooling across technologies such as Elastic, Logstash, Kibana (ELK), Ni‑Fi, Python, geospatial intelligence software and APIs from commercial/open‑source providers.
• Conduct exploratory analysis of datasets to address a range of client problem sets.

Open‑Source Intelligence and Data Exploitation

• Work with a range of datasets in support of open‑source intelligence objectives, applying techniques to improve customer outcomes and highlight dataset shortcomings.
• Attend training to improve tradecraft, techniques, and investigative methods.
• Have a strong foundation in data science, analytics or machine learning with hands‑on experience delivering models that solve practical problems and deliver measurable impact.
• Be comfortable working across the full machine learning lifecycle – from exploratory data analysis and model prototyping to production deployment, integration, and ongoing monitoring.
• Be proficient in Python and its data/ML ecosystem (pandas, scikit‑learn, PyTorch, TensorFlow) and apply statistical and machine learning techniques confidently in real‑world settings.
• Have deployed models into live systems and understand how to make ML operational – whether that involves APIs, application integration or containerisation tools such as Docker.
• Actively monitor the performance of deployed models, using experience in identifying drift, re‑training triggers or optimisation opportunities.
• Stay current with the latest advancements in machine learning and AI and enjoy applying new methods or tools to improve systems and outcomes.
• Be aware of ethical and governance considerations when deploying machine learning at scale (bias, fairness, explainability, compliance) and incorporate these into your work.
• Communicate complex technical work into clear insights and recommendations, adapting the message for both technical and non‑technical stakeholders.
• Work in cross‑functional teams, contributing expertise while collaborating with analysts, engineers, product teams, and decision‑makers.
• Be self‑motivated, solution‑oriented and take ownership of work from scoping to production‑ready delivery.

Due to the nature of our business and requirements of this role, you will need to hold a MoD/Partner DV and be a UK National.