Director, Omics Enabling Platforms

Johnson & Johnson•Spring House, PA

6d•Onsite

About The Position

We are seeking a strategic, hands-on leader to build and direct an Omics Enabling organization that integrates omics with automation, robotics, and agentic AI into a unified engine for AI-Native Drug Discovery. The Director will own end-to-end omics capabilities including transcriptomics (bulk & single-cell) delivered through highly automated, robotics-driven workflows that generate robust, scalable, high-dimensional data at speed. The successful candidate pairs deep expertise in omics technologies with a strong foundation in molecular and cellular biology, enabling interpretation of complex omics datasets and translation of high-dimensional data into biological insights to enable portfolio program decisions. The role combines support for discovery programs with development of reusable, automated experimentation platforms that accelerate learning cycles and generate AI-ready biological data.

Requirements

Minimum of a PhD in Cell Biology, Molecular Biology, Genomics, or a related field, supported by relevant publications is required.
10+ years of experience in cell biology, omics platforms, and computational biology.
Demonstrated ability to derive biological insights from large-scale omics datasets and communicate findings effectively to multidisciplinary scientific teams
Demonstrated experience leading technical and scientific teams
Deep expertise in designing, scaling, and industrializing bulk, single-cell, and spatial transcriptomics capabilities.
Proven experience building robust, high-throughput data generation platforms that deliver reproducible, high-dimensional biological data for discovery, translational science, and AI-enabled research
Strong foundation in molecular and cellular biology with demonstrated ability to translate complex transcriptomic data into mechanistic understanding, disease-relevant hypotheses, biomarker strategies, and actionable portfolio decisions.
Expertise distinguishing biological signal from technical variation and integrating data across experimental systems and disease contexts.
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
Deep understanding of modern transcriptomics analytics, reproducible workflow architectures, cloud/HPC computing, and scalable data engineering.
Experience defining computational strategies that transform high-dimensional biological data into reusable, analysis-ready, and AI-ready data products.
Experience integrating laboratory automation, LIMS/ELN, workflow orchestration, cloud/HPC platforms, and data governance frameworks to enable scalable, reproducible, and AI-ready omics operations.
Expertise in FAIR data principles, metadata strategy, platform lifecycle management, and delivery of governed, reusable data assets.
Strategic thinking with a science-to-impact orientation and crisp communication to technical and executive audiences.
Independent, self-motivated, innovative mindset.
Strong collaboration, planning, prioritization, and problem-solving skills.
Vendor/CRO management and change leadership in fast-moving environments.

Nice To Haves

Big Data Management
Collaboration
Data Governance
Data Reporting
Data Savvy
Developing Others
Drug Discovery Development
Health Care Regulation
Inclusive Leadership
Leadership
Molecular Diagnostics
Pharmaceutical Microbiology
Process Improvements
Product Knowledge
Project Reporting
Scientific Research
Strategic Thinking
Sustainability

Responsibilities

Own development, optimization, and scaling of discovery-focused assays with a transcriptomics core: bulk RNA-seq , single-cell RNA-seq , WGS/WES 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 .
Drive biological interpretation transforming 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 prep, sequencing, and omics data acquisition.
Build closed-loop, autonomous experimentation workflows with robotic orchestration, scheduling, and minimal manual intervention.
Integrate LIMS/ELN, laboratory execution systems, and QC frameworks to ensure reproducibility, throughput, and traceability.
Continuously drive improvements in cycle time, cost per sample, and scalability through automation.
Partner to establish an agentic AI strategy to automate experimental design, protocol generation, data QC, interpretation, and knowledge capture.
Connect AI agents directly to laboratory execution and robotics platforms to enable active learning cycles.
Maintain validated computational pipelines for alignment, quantification, differential expression, fusion/isoform/splicing detection, and single-cell & omics analytics (batch correction, integration, deconvolution).
Champion adoption of large language models, generative, and multimodal AI within discovery workflows.
Own compute strategy (HPC and/or cloud: AWS/GCP/Azure), workflow orchestration (Nextflow/Snakemake/Cromwell), containers, and CI/CD for pipelines.
Build high-throughput pipelines for data acquisition, analytics, and multiomics integration.
Implement FAIR data principles , metadata standards, and privacy/security controls to maximize 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 AI 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 AI disciplines.