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Johnson & Johnson

Makes prescription drugs and medical devices

Director – Omics Enabling Platforms

Translational Research & Bench BiologyConfirmed live in the last 24 hours
Full-timeSpring House, PA, USANo salary listed

About the Job

Requirements

  • A PhD in Cell Biology, Molecular Biology, Genomics, or a related field, supported by relevant publications, is required.
  • At least 10 years of experience in cell biology, omics platforms, and computational biology is required.
  • Demonstrated ability to derive biological insights from large-scale omics datasets and communicate findings effectively to multidisciplinary scientific teams is required.
  • Demonstrated experience leading technical and scientific teams is required.
  • Deep expertise in designing, scaling, and industrializing bulk, single-cell, and spatial transcriptomics capabilities is required.
  • Proven experience building robust, high-throughput data generation platforms that deliver reproducible, high-dimensional biological data for discovery, translational science, and AI-enabled research is required.
  • A strong foundation in molecular and cellular biology and the demonstrated ability to translate complex transcriptomic data into mechanistic understanding, disease-relevant hypotheses, biomarker strategies, and actionable portfolio decisions are required.
  • Expertise distinguishing biological signal from technical variation and integrating data across experimental systems and disease contexts is required.
  • A proven track record building and scaling automated laboratory platforms that integrate robotics, digital orchestration, and closed-loop experimentation to increase throughput, reproducibility, and learning velocity across discovery workflows is required.
  • Deep understanding of modern transcriptomics analytics, reproducible workflow architectures, cloud or high-performance computing, and scalable data engineering is required.
  • Experience defining computational strategies that transform high-dimensional biological data into reusable, analysis-ready, and AI-ready data products is required.
  • Experience integrating laboratory automation, laboratory information management systems and electronic laboratory notebooks, workflow orchestration, cloud or high-performance computing platforms, and data governance frameworks is required.
  • Expertise in FAIR data principles, metadata strategy, platform lifecycle management, and delivery of governed, reusable data assets is required.
  • Strategic thinking with a science-to-impact orientation and clear communication to technical and executive audiences is required.
  • An independent, self-motivated, and innovative mindset is required.
  • Strong collaboration, planning, prioritization, and problem-solving skills are required.
  • Vendor and contract research organization management and change leadership in fast-moving environments are required.

Responsibilities

  • Own development, optimization, and scaling of discovery-focused assays with a transcriptomics core, including bulk RNA sequencing, single-cell RNA sequencing, whole-genome sequencing, whole-exome sequencing, and long-read sequencing.
  • Ensure all assays are automation-ready, reproducible, and standardized for large-scale perturbational biology.
  • Generate high-quality, high-dimensional datasets suitable for actionable insights, machine learning, and foundation model training.
  • Transform large-scale data into mechanistic insights, pathway-level understanding, and actionable biological hypotheses.
  • Lead the design, deployment, and industrialization of laboratory automation and robotic systems across sample intake, library preparation, sequencing, and omics data acquisition.
  • Build closed-loop, autonomous experimentation workflows with robotic orchestration, scheduling, and minimal manual intervention.
  • Integrate laboratory information management systems, electronic laboratory notebooks, laboratory execution systems, and quality-control frameworks to ensure reproducibility, throughput, and traceability.
  • Continuously improve cycle time, cost per sample, and scalability through automation.
  • Partner to establish an agentic artificial intelligence strategy that automates experimental design, protocol generation, data quality control, interpretation, and knowledge capture.
  • Connect artificial intelligence agents directly to laboratory execution and robotics platforms to enable active learning cycles.
  • Maintain validated computational pipelines for alignment, quantification, differential expression, fusion, isoform and splicing detection, and single-cell and omics analytics, including batch correction, integration, and deconvolution.
  • Champion adoption of large language models, generative artificial intelligence, and multimodal artificial intelligence within discovery workflows.
  • Own compute strategy, including high-performance computing and/or cloud platforms such as AWS, Google Cloud Platform, or Azure; workflow orchestration with Nextflow, Snakemake, or Cromwell; containers; and continuous integration and continuous delivery for pipelines.
  • Build high-throughput pipelines for data acquisition, analytics, and multiomics integration.
  • Implement FAIR data principles, metadata standards, and privacy and security controls to maximize data reuse and AI readiness.
  • Deliver reusable, versioned data products and dashboards.
  • Lead an Omics Enabling organization through direct and matrixed leadership, integrating capabilities across automation, robotics, omics, data engineering, and artificial intelligence to accelerate AI-native drug discovery.
  • Foster a culture of safety, quality, scientific excellence, innovation, and rapid learning.
  • Mentor talent and develop career paths across biology, engineering, and artificial intelligence disciplines.

About the Company

Johnson & Johnson

Makes prescription drugs and medical devices

Johnson & Johnson develops prescription medicines and makes medical devices for hospitals, and unlike most big drugmakers, it runs both businesses under one roof. In 2015, its Darzalex became the first antibody drug approved for multiple myeloma, a blood cancer. Its medicines treat cancer, immune diseases such as psoriasis, and mental illnesses such as depression, and patented drugs bring in most of its sales. Its MedTech unit makes heart pumps, catheters that treat irregular heartbeats, surgical sutures, and Acuvue contact lenses. It was founded in 1886 to make sterile surgical dressings, is based in New Brunswick, New Jersey, and spun off its consumer brands, such as Tylenol and Band-Aid, as Kenvue in 2023.

Company size
10,001+
Company stage
IPO
Headquarters
New Brunswick, NJ
Founded
1886

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