Senior Scientist, Multiomics Perturbation

Johnson & Johnson Johnson & Johnson · Pharma · Madrid, Spain +1

Senior Scientist role focused on applying advanced computational models and AI/ML to integrate multi-modal perturbation data and human omics datasets for target/pathway nomination in therapeutic discovery for immune-mediated diseases. The role involves developing predictive frameworks, identifying target combinations, and performing in silico target deconvolution to inform portfolio decisions. Requires expertise in AI/ML for biological datasets, prediction modeling, phenotype scoring, and multi-omics integration.

What you'd actually do

  1. Lead the design, optimization, and validation of human‑omics driven perturbation prediction models integrating CRISPR KO, transcriptomics, proteomics, cytokine profiles, and other cellular screening data to predict disease‑reversal phenotypes in disease models.
  2. Develop advanced computational strategies to perform target deconvolution, identify target combinations and pathway perturbation that most effectively reverse pathogenic activity.
  3. Build human‑omics based phenotype scoring frameworks to quantify perturbation efficacy, validated across internal datasets and public databases.
  4. Collaborate closely with therapeutic discovery, disease biology and preclinical safety leads to translate in vitro perturbation outcomes into relevant human disease biology insights, and to translate computational outputs into experimental validation strategies.
  5. Provide technical leadership — set analytical roadmaps, define milestones, quantify success metrics, and ensure reproducibility and scalability across workflows.

Skills

Required

  • Ph.D. in Bioinformatics, Computational Biology, Data Science, Biomedical Engineering, Computer Science, Artificial Intelligence, or a related field.
  • 2+ years postdoctoral or industry experience in large‑scale multi‑omics data analysis (e.g., genomics, transcriptomics, proteomics), preferably in therapeutic discovery or translational research.
  • Proven expertise in AI/ML modeling for biological datasets, prediction modeling, phenotype scoring, and multi‑omics integration.
  • Experience with systems biology/network analysis
  • Advanced skills in high‑performance/cloud computing, reproducible pipeline development, and workflow optimization.
  • Excellent written and oral communication skills for multidisciplinary teams.
  • Demonstrated record of impactful research (peer‑reviewed publications, conference talks).

Nice to have

  • Experience in _in vitro_ perturbation data analysis and chemical‑target mapping.
  • Knowledge of public omics databases.
  • Background in network/pathway analysis and integrative data harmonization across platforms.
  • Familiarity with immune‑mediated disease biology, especially fibroblast pathobiology.
  • Advanced Analytics
  • Business Intelligence (BI)
  • Coaching
  • Collaborating
  • Critical Thinking
  • Data Analysis
  • Database Management
  • Data Privacy Standards
  • Data Reporting
  • Data Savvy
  • Data Science
  • Data Visualization
  • Econometric Models
  • Process Improvements
  • Technical Credibility
  • Technologically Savvy
  • Workflow Analysis

What the JD emphasized

  • large‑scale multi‑omics data analysis
  • AI/ML modeling for biological datasets
  • prediction modeling
  • phenotype scoring
  • multi‑omics integration
  • systems biology/network analysis
  • high‑performance/cloud computing
  • reproducible pipeline development
  • workflow optimization
  • impactful research (peer‑reviewed publications, conference talks)

Other signals

  • multi-modal perturbation data
  • human omics datasets
  • target/pathway nomination
  • predictive frameworks for disease state reversal
  • synergistic target combinations
  • in silico target deconvolution
  • portfolio decisions in immune‑mediated diseases
  • CRISPR KO, transcriptomics, proteomics, cytokine profiles
  • disease‑reversal phenotypes
  • phenotype scoring frameworks
  • immune‑mediated disease biology