Overview As a Senior Computational Biologist, you will lead computational biology projects within external collaborations with pharma, biotech, and academia. You'll work with biologists, immunologists, ML specialists, and BD teams to design and execute single-cell multi-omics analyses for immuno-oncology and autoimmune disease research. You will communicate results to external partners and advance Immunai's capabilities, including using LLM-based tools. This role is hybrid in New York City and offers high-impact, cross-functional collaboration.
Responsibilities- Lead computational biology efforts for industry and academic partnership projects in immuno-oncology and autoimmune diseases
- Define research objectives and analysis plans with internal and external stakeholders
- Execute rigorous, reproducible data analyses using state-of-the-art methods
- Maintain close collaboration with a multidisciplinary internal team through project lifecycles
- Communicate findings via presentations and data reports to external stakeholders
- Devise and implement new analytical approaches, including LLM-based tooling
- Champion Immunai core values
Key requirements- 8-10 years of multi-omic data analysis experience (bulk and single-cell transcriptomics)
- Strong bioinformatics toolset and ability to develop/test analysis code (R or Python/pandas)
- Experience with machine learning methodologies and applying ML approaches
- Ability to distill complex analyses into clear conclusions with compelling visualizations and presentations
- Experience leading large-scale initiatives and projects; first-author peer-reviewed publications or project lead roles in industry
- Keen interest in biology/immunology with IO and/or autoimmune disease research; CAR-T experience a plus
- Excellent verbal and written communication skills for external collaborations and internal teams
- Hybrid NYC location
- collaborates effectively with diverse teams
- growth mindset
- results-oriented and excelling in delivery
- multi-omic data analysis (bulk and single-cell transcriptomics)
- bioinformatics tools and methods
- machine learning methodologies
