Data Scientist

Role Purpose:To lead data science projects from initial problem statement, through to data exploration, transformation, and the application of enhanced analytics to our existing business processes. In addition, support the Commercial team in transforming the capabilities of data analytics, data engineering and data science. Being a part of the central data team as a dedicated resource to the commercial team, you will drive a new data culture, best working practice, and advanced data methodologies to build, improve and automate solutions to drive commercial excellence in decision-making.
Accountabilities

• Use a flexible, analytical approach to design, develop, and evaluate predictive models and advanced algorithms that lead to optimal value extraction from the data.
• Maintain reproducible model building workflows, document experimental designs and track model performance.
• Create solutions across Commercial, Operations and Overheads to influence business strategy and decision-making.
• Effectively partner with commercial managers across Pricing, Sales, and Ecommerce, to develop long-term strategic growth initiatives, and be a critical team member on project implementation.
• Apply knowledge of statistics, machine learning, programming, data modelling, simulation, and advanced mathematics to recognise patterns, identify opportunities, pose business questions, and make valuable discoveries, leading to prototype development and product improvement.
• End-to-end implementation of data science pipelines.

Experience and QualificationsKnowledge/Skills

• Expertise in tools such as SQL, Azure Data Factory, Azure Databricks, Azure Synapse, R, Python and Power BI.
• Exceptional ability to communicate strategic and technical concepts to a diverse audience and translate business needs into technical requirements
• Experience of liaising effectively with Data Engineers and a thorough understanding of modern software development practices and tools (e.g. testing, containerisation and Azure cloud technologies).
• Working in an agile methodology through the use of Jira and Confluence (Sprints and project management)
• Comfortable challenging stakeholders to migrate flat file data into a consolidated environment, improving data integrity and overall accessibility

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