Senior Data Scientist - Outside IR35 - SC Cleared at SR2 - Socially Responsible Recruitment, London, 6 Months, £550-£600 per day

We are seeking a Senior Data Scientist/AI Innovation Lead to help shape and deliver data science and machine learning innovation within a critical government workstream.

This role will play a key part in driving 2-3 week innovation sprints, leading the development of proof-of-concepts that surface actionable insight to Home Office Tableau users. The successful candidate will combine strong hands-on technical depth with the ability to frame problems, shape experiments, guide delivery, and influence how innovative data science capability is applied in practice.

The focus is on rapidly testing high-value opportunities across areas such as LLM-driven summarisation, machine learning for data quality, anomaly detection, and statistical modelling, while working closely with stakeholders, engineers and analysts in a cloud-first environment.

• Lead the design and execution of data science, AI and ML innovation sprints, translating ambiguous problems into structured experiments and proof-of-concepts.
• Own the development of analytical and machine learning approaches across priority use cases including:
  • LLM-based documentation summarisation
  • machine learning for data quality
  • anomaly detection
  • statistical and predictive modelling
• Shape hypotheses, define success criteria, and determine the most appropriate methods, tools and modelling techniques for each sprint.
• Develop production-aware prototypes using the Python ecosystem, including technologies such as PySpark, PyTorch and associated data science libraries.
• Provide technical leadership across experimentation in AWS, leveraging services such as SageMaker, S3, Athena, Lambda and CloudWatch.
• Ensure outputs are meaningful and usable for downstream Tableau-based insight consumption, working closely with analytics and stakeholder communities.
• Guide data exploration, feature engineering, model evaluation, and interpretation of results, ensuring robust and defensible analytical outputs.
• Work with platform and engineering teams to align innovation delivery with infrastructure standards, including Terraform IaC and migration from Kubernetes pipelines into Terraform-managed infrastructure.
• Advise on the feasibility, scalability and value of proof-of-concepts, helping determine which innovations should be progressed, refined or stopped.
• Engage with stakeholders to explain methods, assumptions, limitations and implications of AI/ML solutions in a clear and credible way.
• Promote good practice in experimentation, reproducibility, documentation, model governance and responsible use of AI.
• Mentor or support more junior practitioners, helping raise capability across the wider team.

• Strong experience in a Data Scientist, Senior Data Scientist, ML Engineer, Applied Scientist or advanced analytics role within a complex delivery environment.
• Deep practical expertise in Python-based data science and machine learning, including tools such as PySpark, PyTorch, pandas, scikit-learn or similar.
• Proven experience shaping and delivering proof-of-concepts, prototypes, or innovation-led analytical solutions from problem definition through to outcome assessment.
• Strong understanding of statistical methods, machine learning techniques and model evaluation approaches.
• Experience applying AI/ML techniques to real-world problems such as anomaly detection, NLP, classification, forecasting, summarisation, or data quality improvement.
• Strong working knowledge of AWS services relevant to data science and ML workflows.
• Ability to operate confidently in ambiguous environments, structure exploratory work, and make sound decisions on technical approach.
• Experience engaging senior stakeholders and clearly communicating technical findings, trade-offs and recommendations.
• Understanding of how analytical outputs can be operationalised, consumed or Embedded within wider reporting and decision-support ecosystems.
• Experience working in Agile, iterative, sprint-based delivery teams.