Senior Data Scientist

Location: People Management: No direct reports, but significant mentoring responsibilities. End-to-End Solution Development: Design and develop data science solutions from problem definition through to implementation and business adoption, balancing model complexity with business value. Model Building & Optimization: Transform complex data sets into models that optimize customer experience, revenue generation, and cost reduction. Stakeholder Influence: Act as a trusted partner to various business domains, translating technical concepts into clear, concise, and understandable recommendations for both technical and non-technical audiences. Technical Best Practices: Adhere to and champion best practices in SQL, Python, and Git within our tech stack (Snowflake, GCP, GitLab). Mentoring & Community: Advise and mentor the wider data and insight community on data science use cases, acting as a subject matter expert (SME). Governance & Ethics: Ensure all work complies with GDPR and adheres to the Partnership's risk management and data privacy policies. Agile Collaboration: Work within multidisciplinary agile teams (ML Engineers, Analysts, Architects) to deliver data-driven products on time and within budget. Machine Learning & Statistics: Proven experience applying statistical and machine learning techniques to solve real-world business problems. Python Proficiency: Expert user of Python and its relevant packages and libraries for data science. Data Manipulation: Strong SQL skills and the ability to link complex data sets for actionable insights. Communication & Influence: Excellent written and verbal communication skills, with a track record of working across functional teams to design technical solutions. Analytical Mindset: A highly analytical approach with a proactive "problem-solving" attitude. Sector Expertise: Previous experience in Retail, Marketing, or E-commerce. Modern Tech Stack: Familiarity with Cloud technologies (GCP/Snowflake) and command-line tools. Software Engineering & MLOps: Experience with the implementation of end-to-end ML solutions and Git version control. Agile Delivery: Experience working in Agile teams and applying Agile methodologies. Bayesian Modelling: Experience working with and implementing Bayesian statistical models in a commercial setting.