Director, Omics

Johnson & Johnson Johnson & Johnson · Pharma · Beerse, Antwerp, Belgium

Director of Omics role at Johnson & Johnson, leading a lab that spans wet-lab assay development (genomics, transcriptomics) and dry-lab computational/bioinformatics. The role focuses on building and evolving genomics strategy, establishing end-to-end pipelines for target discovery and mode of action understanding, and delivering multi-omics integration. Responsibilities include leading the development of various omics assays, owning the compute strategy and building high-throughput pipelines for data acquisition and analysis, and partnering to deliver biological interpretation using ML models and statistical methods. The role also involves people leadership, stakeholder management, and budget oversight.

What you'd actually do

  1. Build and evolve a Genomics strategy that directly supports drug discovery across all phases including pathway mapping, MoA elucidation, resistance mechanisms, and phenotypic screening
  2. Establish assay‑to‑insight pipelines from sample intake to analytics/interpretation and knowledge capture (FAIR data)
  3. Own compute strategy (HPC and/or cloud: AWS/GCP/Azure), workflow orchestration (Nextflow/Snakemake/Cromwell), containers, and CI/CD for pipelines.
  4. Build high-throughput pipelines for data acquisition, analytics and enabling Multiomics data integration
  5. Partner within Multiomics Discovery and across R&D Data Sciences to deliver biological interpretation (pathway/network enrichment, GRNs, causal inference, ML models, signatures, patient stratification)

Skills

Required

  • PhD in Biology, Biotechnology, Immunology, or a related field with a focus on computational biology
  • 10+ years expertise in drug discovery
  • Deep expertise in assay design/optimization
  • Bioinformatics proficiency (R/Python)
  • Workflow managers (Nextflow/Snakemake)
  • Alignment/variant callers (BWA, STAR, GATK, DRAGEN)
  • Single-cell tools (Seurat, Scanpy)
  • Spatial pipelines
  • QC frameworks (FastQC, MultiQC)
  • Transcriptomics data analysis (data processing, differential gene expression, pathway analysis)
  • Data visualization tools and methodologies
  • Machine learning algorithms
  • Statistics

Nice to have

  • Genomics strategy
  • Multi-omics integration
  • Automation, robotics, LIMS integration
  • Cloud platforms (AWS/GCP/Azure)
  • CI/CD
  • Data governance & security (FAIR, MIxS, MINSEQE; PII/PHI controls; role‑based access; audit trails)
  • Biological interpretation (GRNs, causal inference, ML models, signatures, patient stratification)
  • Reusable data products and dashboards
  • Budgeting, capacity planning, utilization, and cost‑per‑sample optimization
  • Vendor/consumables contracts, service agreements, and supply chain continuity
  • Biosafety, EHS, data privacy (HIPAA where applicable), IP stewardship, and security best practice

What the JD emphasized

  • deep understanding of omics technologies, biology, and strong computational biology skills
  • Established track record in using computational biology to address complex biology questions is required
  • Proficiency in programming languages such as R or Python is required
  • Strong communication and presentation skills are required
  • 10+ years expertise in drug discovery is required

Other signals

  • ML models
  • data integration
  • bioinformatics pipelines
  • computational biology