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Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.

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Johnson and Johnson Innovative Medicine is recruiting aPrincipal Developer, Oncology Data & AI Systems (2 available positions) to strengthen the AI-ready data foundation that powers Oncology R&D. This role will lead efforts to modernize and standardize data capture, translate scientific and business needs into engineering requirements, and design, build, and optimize scalable data pipelines and workflows that ensure data is high-quality, well-governed, interoperable, and fit for advanced analytics and AI/ML.

This role focuses on data science initiatives across Oncology R&D 6 including Clinical, Pre-Clinical, RWD and omics platforms by enabling trusted, reusable datasets and foundational data products that accelerate downstream insights and model development.This role will be a leading data science contributor and creative problem solver with developing AI-ready data and other routinely used data applications that improve speed, reliability and impact of data-driven decision making for Oncology R&D.

This position will be located in either Cambridge, MA; Spring House, PA; Titusville, NJ; Raritan, NJ; or San Diego, CA (no remote option).

Key Responsibilities:

• Partner with Oncology R&D and Data Science stakeholders to identify, prioritize, and deliver high-impact data, AI/ML, and GenAI use cases that accelerate scientific discovery, study execution, and evidence generation.
• Own the end-to-end design, build, and lifecycle management of Oncology R&D data products, including requirements, architecture, ETL/ELT development, documentation, and operational support.
• Integrate and harmonize multi-modal R&D data across biomarker labs, translational platforms, clinical trials, real-world data/evidence (), genomics/other omics, and pre-clinical research systems to create trusted, reusable, AI-ready datasets.
• Co-develop an AI-ready data ecosystem in collaboration with Data Product Engineers, Data Scientists, Knowledge Graph Engineers, Enterprise Architecture, and IT 6enabling advanced analytics, ML, and GenAI applications.
• Translate complex scientific, clinical, and operational needs into scalable engineering solutions, ensuring alignment with Oncology R&D priorities, enterprise data standards, and interoperability requirements.
• Design and optimize data pipelines for structured and unstructured data, leveraging Python, R, SQL, AWS services, and other relevant technologies to improve throughput, reliability, and maintainability.
• Implement robust data quality and reliability controls, including validation frameworks, automated monitoring, and KPI-driven measurement of data product performance, adoption, and business impact.
• Establish strong data governance foundations by maintaining data lineage, metadata, and versioning practices that support transparency, traceability, reproducibility, and compliance with internal and regulatory expectations. Embed FAIR data principles and compliance requirements into workflows

Required Qualifications:

• Bachelors Degree in Computer Science, Engineering, Life Sciences, or other relevant field. Advanced degree preferred
• 5+ years of experience in data engineering, including data modeling, schema design and database architecture, preferably in the healthcare industry
• Demonstrated proficiency in data engineering tools such as Python, R and SQL for data processing, transformation, and automation across large-scale datasets
• Hands-on experience designing and operating cloud-based data platforms, preferably on AWS (e.g., Redshift, FSx, Glue, Lambda) or equivalent services
• Experience working with multiple data storage patterns, including relational, NoSQL/unstructured, and graph data technologies (e.g., knowledge graphenabling platforms). Strong analytical and problem-solving skills, with the ability to troubleshoot complex data pipeline, quality, and performance issues in production environments.
• Proven capability to lead cross-functional delivery and continuous improvement initiatives with multidisciplinary, distributed teams; experience coordinating with external vendors/partners.
• Demonstrated strength in stakeholder management, including requirements discovery, business analysis, and planning; ability to translate conversations into clear user stories, engineering requirements, and executable delivery plans.
• Ability to manage multiple concurrent projects, prioritize work, exhibit organizational skills and flexibility to deliver maximum business value.
• Willingness to conduct periodic travel (<15% of time) to conferences and internal meetings.

Preferred Qualifications:

• 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 GenAI model deployment.

This position will be located in either Spring House, PA; Titusville, NJ; Raritan, NJ; San Diego, CA; or Cambridge, MA, (no remote option), and may require up to approximately 10% travel.

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