Manager, Data Science - Oncology

Johnson & Johnson Johnson & Johnson · Pharma · Spring House, PA +7

Manager, Data Science - Oncology role at Johnson & Johnson focuses on designing, developing, and maintaining data pipelines for Oncology R&D data. This includes acquiring, managing, and storing data from diverse sources, creating AI-ready data systems, and implementing data models and quality standards. The role involves people leadership and hands-on contribution, leveraging cloud technologies like AWS and tools such as Python, R, and SQL. Experience with healthcare data standards and MLOps is preferred.

What you'd actually do

  1. Serve as both a people leader and a hands-on contributor for designing, developing and maintaining data pipelines for acquiring, managing and storing Oncology R&D data from diverse sources (e.g. biomarker labs, real-world data sources, pre-clinical applications)
  2. Work closely with Data Science and Oncology R&D partners to understand, document and prioritize business requirements. Translate these business needs in to high quality data products.
  3. Work closely with other technical leaders, such as Ontology and Knowledge graph Engineers to design and deliver future-proof, AI-ready data systems aligned with Oncology R&D business needs.
  4. Develop Oncology R&D-specific data repositories by implementing standard enterprise-level data models and create new data models as needed. Leverage cloud-based technology platform to accomplish goals, such as building and maintaining data repositories using AWS S3.
  5. Create and optimize data flows for structured and unstructured data using technologies such as Python, R, SQL, AWS services and other relevant tools.

Skills

Required

  • 5+ years of experience in data engineering, including data modeling and database design
  • 2+ years experience managing a technical team aimed at delivering data systems
  • Proficiency in data engineering tools such as Python, R and SQL for data processing
  • cloud architecture (e.g. AWS services, Redshift, FSx, Glue, Lambda)
  • Experience with unstructured database technologies (e.g. NoSQL) as well as other database types (e.g. Graph)
  • Strong skills in analysis, problem-solving, organizational change, project delivery, and managing external vendors.
  • Proven record leading improvement initiatives with multi-disciplinary and remote partners.
  • Demonstrated stakeholder management capabilities- including requirements gathering, business analysis and planning.
  • Ability to manage a numerous projects simultaneously, prioritize work, exhibit organizational skills and flexibility to deliver maximum business value.

Nice to have

  • Advanced degree (Master’s or equivalent) in Computer Science, Engineering, Life Sciences, or other relevant field is strongly preferred.
  • preferably in the healthcare industry
  • preferably in the healthcare industry
  • Experience with healthcare data standards (e.g. CDISC, HL7, FHIR, SNOMED CT, OMOP, DICOM).
  • Exposure to high dimensional data technologies and handling, including imaging.
  • Familiarity with machine learning operations (MLOps) and model deployment.

What the JD emphasized

  • designing and implementing engineering requirements
  • developing AI-ready data
  • designing and delivering future-proof, AI-ready data systems
  • implementing standard enterprise-level data models
  • implementing quality and performance standards
  • implementing software development best practices