Manager, Data Science - Oncology

Job Title

Manager, Data Science – Oncology

The Data Science and Digital Health team invites you to contribute to data capture, build and optimize data workflows, and design engineering requirements that support Oncology R&D. You will work on data projects across Clinical, Pre‑Clinical, Real‑World Data and ‘omics platforms, and develop AI‑ready data and routine data applications for Oncology R&D.

• Serve as a people leader and hands‑on contributor for designing, developing and maintaining data pipelines that acquire, manage and store Oncology R&D data from diverse sources.
• Partner with Data Science and Oncology R&D teams to document, prioritize and translate business requirements into high‑quality data products.
• Collaborate with other technical leaders, such as Ontology and Knowledge‑Graph engineers, to design future‑proof, AI‑ready data systems aligned with business needs.
• Develop Oncology R&D‑specific data repositories by implementing standard enterprise‑level data models and creating new models as needed, using cloud‑based platforms such as AWS S3.
• Create and optimise data flows for structured and unstructured data using Python, R, SQL, AWS services and related tools.
• Implement quality and performance standards and measure KPIs to ensure accuracy and consistency.
• Use data versioning and lineage tracking to support traceability, compliance, and maintain documentation of data architectures and workflows.
• Adhere to internal standards and implement software‑development best practices such as code versioning and Dev Ops.

• Advanced degree (Master’s or equivalent) in Computer Science, Engineering, Life Sciences, or a related field (Bachelor’s degree with equivalent experience may be considered).
• Five or more years of experience in data engineering, with a focus on data modelling and database design, preferably in the health‑care industry.
• Two or more years of experience managing a technical team that delivers data systems, preferably in health‑care.
• Proficiency in data engineering tools such as Python, R, SQL, and cloud architecture (e.g., AWS services, Redshift, FSx, Glue, Lambda).
• Experience with unstructured database technologies (No
  
  SQL) and other database types (Graph).
• Strong skills in analysis, problem‑solving, organisational change, project delivery, and external‑vendor management.
• Proven record of leading improvement initiatives with multi‑disciplinary and remote partners.
• Demonstrated stakeholder‑management capabilities, including requirements gathering, business analysis, and planning.
• Ability to manage numerous projects simultaneously, prioritise work, and deliver maximum business value.
• Willingness to travel (