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.
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