- Provides data science and data analyst expertise to a range of government and private sector clients. - Work with customers to understand and deliver against their requirements, communicating complex findings and insights in a clear and concise manner to both technical and non-technical stakeholders. - Develop predictive models, perform statistical analysis and apply machine learning algorithms to extract insights from large and complex datasets using programming languages such as Python. Conduct data exploration and visualisation, using tools such as Python and Power BI to understand complex and varying datasets, as a prelude to data quality assessments and data cleansing activities. - Work closely within/alongside teams of data scientists, data analysts, data engineers/architects and business analysts using agile methodology to progress projects and build analytical models. - Lead small teams of 1-2 additional data scientists/analysts to progress work and produce insights. - Conduct literature reviews and research to drive forward capabilities and knowledge, including a research project on “Explainable AI” - focused on identifying, understanding and mitigating bias in foundational AI models.

- Provided both tactical and strategic analysis to support investigation and business needs. - Assessed how best to approach tasking from business managers, using technical knowledge and judgement to clarify and refine requirements, and to select the most effective tools and techniques to deliver analytical output within clearly defined timescales. - Cross-matched bulk datasets, using bespoke and COTS software applications, including Hue, SAS Enterprise Guide, ArcGIS and Python to conduct multi-factorial analysis, providing customers with detailed analysis to inform decisions. - Identified gaps in existing analytical capability and devised solutions in conjunction with relevant technical team. - Produced reports in format appropriate for non-technical customers, using simple yet informative language and a range of visuals, presentation tools, graphs and charts. - Data Science champion, responsible for identifying opportunities to apply data science techniques, liaising with data science team and producing data science models to answer customer requirements and drive forward analytical capability. - Acted as a mentor to junior team members, providing support and advice as necessary and aiding to upskill colleagues to meet team requirements