Senior Manager, Oncology Data and AI Systems

Johnson & Johnson Johnson & Johnson · Pharma · Cambridge, MA +7

Senior Manager to lead the design, development, and operation of scalable, AI-enabled data products in Oncology R&D. This role involves integrating diverse data sources into AI-ready assets, shaping and delivering AI systems to accelerate decision-making, and collaborating with domain experts to ensure future-proof, AI-ready data systems. The position requires proficiency in data engineering tools, cloud platforms, and agentic AI coding tools, with a focus on building data pipelines and AI workflows.

What you'd actually do

  1. Serve as both a people leader and a hands-on contributor accountable for the end-to-end design, development, and operation of scalable, AI-enabled data products supporting Oncology R&D.
  2. Drive integration of diverse data sources (e.g., biomarker labs, real-world data, pre-clinical and translational systems) into high-quality, reusable, AI-ready assets.
  3. Partner closely with Oncology R&D and Data Science stakeholders to shape, prioritize, and deliver against business-critical use cases. Translate complex scientific and operational needs into robust data products and AI systems that accelerate decision-making, improve study execution, and enhance probability of success.
  4. Collaborate with domain experts, such as Ontology, Knowledge graph, and Data Product Engineers to design and deliver future-proof, AI-ready data systems aligned with Oncology Strategy. Ensure solutions enable discoverability, reuse, and advanced analytics/GenAI applications.
  5. Lead team in the development of Agile practices and agentic AI coding tools.

Skills

Required

  • 6+ years of experience in data engineering
  • data modeling
  • database design
  • Python
  • SQL
  • cloud architecture (e.g. AWS services, Redshift, FSx, Glue, Lambda.)
  • unstructured database technologies (e.g. NoSQL)
  • Graph databases
  • analysis
  • problem-solving
  • organizational change
  • project delivery
  • managing external vendors
  • stakeholder management
  • requirements gathering
  • business analysis
  • planning
  • managing numerous projects simultaneously
  • prioritize work
  • organizational skills
  • flexibility

Nice to have

  • Advanced degree (Master’s or equivalent) in Computer Science, Engineering, Life Sciences, or other relevant field
  • healthcare industry experience
  • Model Context Protocol (MCP) servers
  • high dimensional data technologies and handling, including genomics and imaging
  • machine learning operations (MLOps)
  • model deployment
  • healthcare data standards (e.g. CDISC, HL7, FHIR, SNOMED CT, OMOP, DICOM)

What the JD emphasized

  • end-to-end design, development, and operation of scalable, AI-enabled data products
  • high-quality, reusable, AI-ready assets
  • robust data products and AI systems
  • advanced analytics/GenAI applications
  • AI workflows
  • agentic AI coding tools

Other signals

  • building scalable AI-enabled data products
  • integration of diverse data sources into AI-ready assets
  • deliver robust data products and AI systems
  • advanced analytics/GenAI applications
  • AI workflows for structured and unstructured data
  • agentic AI coding tools