About the job
Are you a highly motivated and detail-oriented computational biologist passionate about understanding complex biological systems and disease through single-cell omics and large-scale patient datasets? This role offers a unique opportunity to contribute to the development and application of Cellarity’s platform. Specifically, you will identify disease-associated states from cell atlases and use our single-cell AI models to predict chemical entities that revert these states to healthy.
You will be part of a dynamic and innovative computational team focused on generating high-impact insights. Collaborating closely with world-class computational biologists, machine learning scientists, experimental biologists, chemists, clinicians, and technologists, you’ll help co-develop hypotheses, build proprietary data assets, and advance exploratory drug programs.
If you’re excited about applying your skills to real-world therapeutic challenges, working with proprietary, high-quality datasets, and learning from leading experts across disciplines, we want to hear from you.
Responsibilities
Apply computational biology and machine learning tools to generate insights and hypotheses from high-dimensional molecular datasets, with a focus on large-scale scRNA-seq & snRNA-seq disease atlases.
Develop & lead data analysis strategies and ideation across diverse biological and clinically relevant projects with internal and external stakeholders.
Act as the computational biology co-lead on drug programs, working with experimental scientists to ensure that study designs & data are properly interrogated to deliver high value readouts.
Present your results in an interdisciplinary team of biologists, chemists, clinicians, technologists, and other machine learning colleagues in meetings covering cross-functional project teams, functional teams, to whole company and management meetings.
Qualifications
Minimum
Ph.D. or equivalent in biology, computational biology, or related scientific field.
2+ years of relevant experience post Ph.D.
Extensive experience in analyzing and deriving hypothesis from single-cell transcriptomic data, preferably also with large-scale single-cell atlases.
Demonstrated scientific understanding of molecular and systems biology, diverse molecular data types, and analysis tools.
Practical programming and scripting skills, preferably in Python.
Fast learner, analytical thinker, creative, "hands-on", strong communication skills.
Able to work both independently and as part of a team.
Preferred
Background in metabolic or liver diseases, autoimmune disease, or hematology.
Experience with generating, analyzing, and deriving hypotheses from other single-cell omics data types or large-scale clinical datasets.
Experience driving an impactful and relevant project within industry.
Background in statistics or machine learning.
Experience with clinical or biological data.
Interested in learning any of the above.