Overview In this role you drive integration and interpretation of large multimodal datasets, with emphasis on single-cell and spatial omics in immunology, to identify disease mechanisms, biomarkers, and therapeutic hypotheses. You will lead cross-functional collaboration, apply AI-driven approaches, and shape precision medicine propositions across the Immunology portfolio from discovery to clinical development. You'll ensure AI outputs are biologically valid and translationally relevant while pushing methodological innovation to accelerate medicines for patients.
Compensation / Benefits- base salary range $143,323.20 - $214,984.80
- short-term incentive bonuses
- equity-based awards for salaried roles
- retirement programs
- paid time off (vacation, holiday, leaves)
- health, dental, and vision coverage
Responsibilities- Lead integration and interpretation of large multimodal datasets (single-cell, spatial omics, immune repertoire, high-dimensional profiling)
- Define innovative analytical and AI-driven approaches to generate hypotheses and biomarkers
- Develop and champion precision medicine propositions to advance therapeutics
- Plan, execute, and communicate analyses to maximize value of data assets across discovery to clinical development
- Evaluate and apply AI/ML methods responsibly, ensuring biological validity and translational relevance
- Collaborate with immunology, cell therapy, translational medicine, and AI experts to guide data-driven decisions
- Provide scientific leadership and quality oversight for AI outputs in translational contexts
- Drive cross-functional alignment and effective communication of complex analyses to diverse stakeholders
- Mentor or guide junior team members in computational methods and project delivery
Key requirements- PhD plus 6+ years of applicable experience in academia, biotech, or pharma R&D
- Substantial immunology or cell therapy research experience with translational impact
- Deep expertise in single-cell omics analysis and translation to biology/therapy
- Strong experience in spatial omics (spatial transcriptomics/proteomics)
- Experience with BCR/TCR repertoire analysis or CyTOF; cross-domain interest
- Proven ability to perform multimodal data integration across molecular and clinical data
- Excellent Python/R coding skills; proficiency with Git/Bitbucket, Linux; experience with AI-assisted coding tools (GitHub Copilot, Claude Code)
- Experience applying ML/AI to biological data; familiarity with foundation models, deep learning (PyTorch/TensorFlow), and LLMs for knowledge extraction
- Ability to critically evaluate AI outputs in biological/translational context
- Outstanding communication, collaboration, and influence with senior stakeholders
- High degree of scientific independence, strategic judgement, curiosity, delivery mindset
- strong communication and storytelling
- collaboration across cross-functional teams
- influencing and stakeholder management
- single-cell RNA-seq analysis
- spatial omics/spatial transcriptomics
- BCR/TCR repertoire analysis
