Data Scientist/ML Analyst - Outside IR35 - SC Cleared

We are seeking a Data Scientist/ML Analyst to support a critical government data and analytics workstream focused on innovation, experimentation, and rapid proof-of-concept delivery.

Working as part of a multidisciplinary team, you will contribute to 2-3 week innovation sprints aimed at developing and testing new approaches to help surface insight to Tableau users across a complex operational environment. The work will sit at the intersection of data science, machine learning, AI experimentation, analytics enablement, and cloud-based engineering.

This is an opportunity for someone who enjoys hands-on problem solving, prototyping, and translating data into practical use cases that can improve decision-making in high-profile environments.

Key Responsibilities

• Support the design and delivery of data science and AI proof-of-concepts within short innovation sprint cycles.
• Build, test and iterate models and analytical approaches across use cases such as:
  • LLM-based documentation summarisation
  • machine learning for data quality improvement
  • anomaly detection
  • statistical modelling
• Explore, prepare and analyse structured and unstructured data to identify patterns, issues, and opportunities for insight generation.
• Develop robust Python-based analytical workflows using tools across the wider Python ecosystem, including libraries and frameworks relevant to machine learning and data processing.
• Contribute to experimentation in AWS-based environments, using services such as SageMaker, S3, Athena, Lambda and CloudWatch.
• Help surface model outputs and analytical findings in a way that can be consumed by Tableau users and wider stakeholders.
• Work closely with engineers, analysts and stakeholders to understand business problems and turn them into testable hypotheses and data science solutions.
• Support model evaluation, validation and documentation, ensuring outputs are explainable, proportionate and usable.
• Contribute to infrastructure-aware delivery, including working within environments using Terraform IaC and supporting migration from Kubernetes-based pipelines into Terraform-managed infrastructure.
• Assist with documenting methods, assumptions, limitations and recommended next steps following sprint activity.

Skills and Experience Required

• Experience working in a data science, machine learning, advanced analytics or data analyst role in a complex environment.
• Strong hands-on capability with Python and relevant data science/machine learning tooling such as PySpark, PyTorch, pandas, scikit-learn or similar.
• Experience building and testing analytical models or proof-of-concepts using statistical, ML or AI techniques.
• Understanding of common data science use cases such as anomaly detection, predictive modelling, NLP or data quality analysis.
• Exposure to cloud-based data and ML tooling, ideally within AWS.
• Ability to interrogate datasets, assess data quality, and prepare data for experimentation and analysis.
• Experience communicating technical findings clearly to non-technical or mixed audiences.
• Comfortable working in Agile or sprint-based delivery environments with changing priorities and exploratory work.
• Awareness of reproducible workflows, good analytical documentation, and governance considerations in data and AI delivery.
• Experience supporting visualisation or downstream reporting outputs, ideally where insights are consumed in tools such as Tableau.