Senior Data Scientist

About the Role

Cancer Research UK (CRUK) is seeking 2 Senior Data Scientists to join their Marketing, Fundraising & Engagement (MFE) directorate. The roles are critical in enabling data-driven decisions that drive impact towards CRUK's mission to beat cancer. You will contribute to a bold transformation programme aimed at better harnessing data and digital marketing technology to deliver relevant, trusted, and frictionless experiences for their audiences. This includes high-impact projects like the London Marathon initiative, using machine learning to identify high-income supporters, and developing forecasting models for the Legacies team.

What will I be doing?

• Lead ML/AI projects with stakeholders across CRUK, documenting objectives and requirements.
• Develop data and modelling initiatives, leveraging industry best practice and internal compliance frameworks.
• Coach data scientists in ML/AI methodologies to foster knowledge growth within the team.
• Implement models using a robust MLOPs process, from ingestion and modelling to ongoing monitoring and performance.
• Ensure correct experimentation and measurement approaches for all ML/AI initiatives.
• Deliver LLM capabilities into CRUK, such as summarisation tools and smart search.
• Collaborate with team members to create a high-performance culture, sharing knowledge in Python, via AWS Sagemaker/Snowpark and other tools.
• Build, develop, and manage relationships with key stakeholders and networks, ensuring departmental work meets needs and builds capability.

What are we looking for?

• Related degree in computer science, mathematics, or related STEM field, or equivalent work experience.
• Demonstrable hands-on skills and experience in technical coding language and data visualisation tools (e.g., SQL, Python, Snowflake, PowerBI, Databricks, GA), providing and implementing best practice guidance and standards.
• Experience using statistical analysis to understand and drive value from consumer behaviour, including setting up supervised & unsupervised learning models, covering data cleaning, data analytics, feature creation, model selection, performance metrics & visualisation.
• Hands-on experience applying MLOps principles (e.g., Snowpark, MLFlow, Github).
• Experience in creating and developing high-performing experimentation analytical support (test and learn, multivariate tests, ML optimisation, automations).
• Experience in a large-scale organisation within a matrixed environment, with essential skills in influencing and managing stakeholders to bring data science to life.
• Understanding of recommendation systems would be beneficial but isn’t essential.