Senior Computational Biologist

Probably Genetic•San Francisco, CA

1d•$160,000 - $200,000•Hybrid

About The Position

Probably Genetic is hiring a Senior Computational Biologist to own the end-to-end processing and interpretation of its genetic data. The company has built a deeply phenotyped genetic disease cohort by helping patients access genetic testing and is using this database for target discovery. The role will take data from quality control through positive-control recovery to novel causal and direction-of-effect target hypotheses, and will help guide which new data modalities the company collects. Results will also support machine-learning researchers training models on multimodal data and advancing target discovery. This is a hands-on, first-of-its-kind role reporting to the VP of R&D.

Requirements

PhD in statistical genetics, computational biology, bioinformatics, or a similar field, plus at least 4 years of post-PhD experience in academia or industry.
A proven track record, demonstrated through shareable projects or publications, of hands-on experience processing, analyzing, and interpreting genetic or genomic data.
Working knowledge of public human genetic and functional genomics resources such as gnomAD, GTEx, Open Targets, and ClinVar.
Strong Python skills.
Experience with workflow managers such as Snakemake.
Comfort working with cloud-scale genomic data resources.
Sufficient machine-learning fluency to collaborate closely, support data preparation, and participate in productive discussions.
Empathy and respect for patients, including marginalized populations.
Patient-focused motivation to develop treatments and make a lasting positive impact.
Comfort working in a fast-paced, often ambiguous environment with rapid change.
Action-oriented approach and enthusiasm for building a company from the ground up.

Nice To Haves

Target discovery or human genetics experience in biotech or biopharma.
Experience with genetic disease and/or disease-enriched cohorts.
Multimodal or multi-omics experience, such as transcriptomics or proteomics.

Responsibilities

Take over, improve, and fully automate the in-house genetic data analysis pipeline, including data processing and quality control.
Run power analyses and use the findings to inform phenotype and indication prioritization, database scaling, and data modality collection.
Demonstrate that the data and pipelines recover known gene-phenotype associations and approved target-indication pairs, and replicate findings against public resources.
Conduct association and causal gene mapping in a selected indication, including rare variant burden, colocalization, and Mendelian randomization analyses.
Partner with machine-learning researchers on data preparation, multimodal integration, and shared evaluation.
Hand off prioritized targets for wet-lab validation and inform in-vitro experimental plans and priorities.