Data Scientist

hackajob is partnering directly with BAE Systems Digital Intelligence to hire for this role. 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. 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…