Statistical Geneticist
Planet Pharma · Cambridge, MA
CONTRACT
Posted Sep 8, 2026
Job Description
Open Market Rate
Summary Of Position
We seek a statistical geneticist to leverage cutting-edge human genetic datasets and methodologies to identify and validate therapeutic targets and biomarkers to support pipeline.
Position Responsibilities
- Leverage existing human genetic association studies and post-GWAS methods (e.g. co-localization, fine-mapping, Mendelian Randomization) to identify causal disease pathways and therapeutic hypotheses
- Integrate multi-trait genetic association, functional annotations, and ‘omics data to elucidate molecular mechanisms and tissue-specific biology
- Characterize variants, genes, and pathways of interest via common and rare variant association analyses in biobank-scale studies (e.g. UK Biobank, All of Us)
Candidate Requirements
- PhD in Statistical Genetics or a related field with 3+ years of relevant postdoctoral or industry experience
- Scientific rigor, strong verbal and written communication skills, and self-motivation are critical
- Deep expertise in statistical genetics, including GWAS and RVAS methods, and hands-on experience with relevant software
- Deep expertise in post-GWAS analyses, including statistical fine-mapping, colocalization, and Mendelian Randomization, and hands-on experience with relevant software
- Experience cleaning, processing, and analyzing individual-level, biobank-scale genetic and phenotypic data; practical experience implementing workflows on UKB RAP and All of Us Researcher Workbench highly preferred
- Expertise in phenotype generation and cross-biobank phenotype curation and harmonization a plus
- Proficiency with unix, advanced hands-on experience in Python and R, and code documentation and version control (e.g. GitHu
Summary Of Position
We seek a statistical geneticist to leverage cutting-edge human genetic datasets and methodologies to identify and validate therapeutic targets and biomarkers to support pipeline.
Position Responsibilities
- Leverage existing human genetic association studies and post-GWAS methods (e.g. co-localization, fine-mapping, Mendelian Randomization) to identify causal disease pathways and therapeutic hypotheses
- Integrate multi-trait genetic association, functional annotations, and ‘omics data to elucidate molecular mechanisms and tissue-specific biology
- Characterize variants, genes, and pathways of interest via common and rare variant association analyses in biobank-scale studies (e.g. UK Biobank, All of Us)
Candidate Requirements
- PhD in Statistical Genetics or a related field with 3+ years of relevant postdoctoral or industry experience
- Scientific rigor, strong verbal and written communication skills, and self-motivation are critical
- Deep expertise in statistical genetics, including GWAS and RVAS methods, and hands-on experience with relevant software
- Deep expertise in post-GWAS analyses, including statistical fine-mapping, colocalization, and Mendelian Randomization, and hands-on experience with relevant software
- Experience cleaning, processing, and analyzing individual-level, biobank-scale genetic and phenotypic data; practical experience implementing workflows on UKB RAP and All of Us Researcher Workbench highly preferred
- Expertise in phenotype generation and cross-biobank phenotype curation and harmonization a plus
- Proficiency with unix, advanced hands-on experience in Python and R, and code documentation and version control (e.g. GitHu
Additional Details
- City
- Cambridge
- State
- Massachusetts
- Country
- US
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