Senior Scientist Omics - Imm

Johnson & Johnson Johnson & Johnson · Pharma · Spring House, PA +1

Senior Scientist role focused on analyzing large-scale clinical, genetic, omics, and cell phenotype data to identify and evaluate drug targets for the Immunology therapeutic area. The role involves constructing analytic frameworks, applying statistical modeling and AI/ML methods, and using NLP to annotate targets and biomarkers. This is a research-focused position within healthcare, utilizing AI/ML for drug discovery and development.

What you'd actually do

  1. Construct an analytic framework by integrating perturbation, ontology and cellular phenotyping with molecular based datasets including genomics, transcriptomics, proteomics, to further enhance target evaluation, and patient stratification.
  2. Provide scientific technical expertise in modeling multimodal data (including genetic, perturbation and functional data) to predict qualities and liabilities associated with on- and off-target treatment effects.
  3. Apply or develop a wide variety of statistical modeling and AI/ML methods to analyze phenomic, multiomic and biobank level data to identify targets associated therapeutic with clinical and/or molecular segments and biomarkers associated with immune mediated diseases.
  4. Provide design and analysis consultation for questions of interest to our Immunology stakeholders and facilitate interactions between colleagues across the organization.
  5. Work cooperatively with different teams in Data Sciences and Therapeutic Discovery teams to implement NLP or related approaches to annotate targets, biomarkers and networks to complement classifier construction.

Skills

Required

  • Ph.D. in quantitative Biology (Bioinformatics, Computational Biology, Systems Biology, Statistical Genetics)
  • 2-3 years post-PhD experience
  • Multi-omic data analysis (RNA, proteomics, epigenomics, genetics, bulk and single cell)
  • Programming and computational skills
  • Communication, organizational, and leadership skills
  • Publication and cross-functional collaboration track record
  • Independent, self-motivated, innovative work style

Nice to have

  • Immunology experience
  • Working knowledge of databases and analytic approaches for gene/pathway ontology (network analysis)
  • Expertise in perturbation data and analysis
  • Experience with advanced analytical approaches (NLP, ML/AI, LLM) for extracting annotation from unstructured data
  • Experience with predictive models for target engagement and druggability
  • Experience with drug discovery and development practices in a pharmaceutical setting

What the JD emphasized

  • Ph.D. in a quantitative Biology field – Bioinformatics, Computational Biology, Systems Biology, Statistical Genetics, or related fields is required.
  • 2-3 years experience post PhD degree is required.
  • Experience with multi-omic data (RNA gene expression, proteomics, epigenomics, genetics) at both bulk and single cell level is required.
  • Proficient in programming and computational skills and demonstrated ability to work with a group of scientists to deliver on objectives in defined timelines, are required.
  • Outstanding communication, organizational and leadership skills, with a successful track record of publication and collaboration with cross-functional scientific teams are required.
  • Independent, self-motivated, innovative, and able to excel in a goal-oriented, multifaceted and fast-moving team environment are required.

Other signals

  • integrating perturbation, ontology and cellular phenotyping with molecular based datasets
  • linking molecular targets to disease states/characteristics
  • validate predicted mechanisms systematic on-/off-target toxicity predictions
  • network-based models, machine learning, and novel algorithmic approaches to mining biological data
  • modeling multimodal data to predict qualities and liabilities associated with on- and off-target treatment effects
  • analyze phenomic, multiomic and biobank level data to identify targets associated therapeutic with clinical and/or molecular segments and biomarkers associated with immune mediated diseases
  • implement NLP or related approaches to annotate targets, biomarkers and networks