Senior Data Scientist

Overview

As a Senior Data Scientist you will lead AI/ML initiatives for government and national security projects, turning data into robust, production-ready solutions. You will work across diverse data types, collaborating with ML engineers, delivery teams and customers to shape use cases and deliver impact. The role blends hands-on modelling with customer engagement, thought leadership and mentoring, contributing to a culture of ethical, high-quality scientific work. This position offers growth in a specialist AI and data science team supporting critical public sector outcomes.

Responsibilities

• Act as a senior technical authority for AI and data science across projects, bids and stakeholder discussions
• Lead and deliver complex ML/AI projects with ML engineers, delivery teams and customers
• Engage with users to define requirements, scope AI use cases and develop delivery plans
• Support design, development and deployment of models within AWS cloud environments
• Contribute to proposals and bid responses with literature research
• Present technical findings to internal teams and external stakeholders
• Mentor junior data scientists and graduates, supporting capability growth
• Promote AI knowledge across multidisciplinary teams and represent the organisation at blogs, events and conferences

Key requirements

• Several years’ experience as a Data Scientist or in a closely related role
• Expert knowledge of ML/AI and statistical methods across NLP, CV, audio, graphs, time-series or tabular data
• Strong Python skills with libraries such as pandas, NumPy, scikit-learn, PyTorch, Transformers, statsmodels, PyMC
• Understanding of software engineering and ML best practices including version control, packaging and model deployment
• Experience training and deploying models on cloud platforms, particularly AWS
• Ability to communicate complex ideas to technical and non-technical audiences
• Commitment to high-quality, ethical and robust scientific work
• Awareness of ethical, privacy, security and policy considerations in applied AI
• Security clearance eligibility or possession is required

• Clear communication with technical and non-technical audiences
• Mentoring and capability development
• Thought leadership and cross-functional collaboration
• NLP
• Computer vision
• Audio processing

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