Statistical Geneticist

Planet Pharma; Cambridge (MA); On-site; USD 140,000 - 190,000; Full time

Job summary

Planet Pharma is seeking a Statistical Geneticist to leverage cutting-edge human genetic datasets and methodologies to identify and validate therapeutic targets and biomarkers to support pipeline.

The role focuses on applying co-localization, fine-mapping, and Mendelian Randomization to identify causal disease pathways and integrate multi-omics data for tissue-specific biology.

Qualifications

• PhD in Statistical Genetics or related field required.
• 3+ years postdoctoral or industry experience preferred.
• Strong scientific rigor and communication skills essential.
• Deep expertise in statistical genetics including GWAS and RVAS.
• Experience with post-GWAS analyses: fine-mapping, colocalization, Mendelian Randomization.
• Experience cleaning and analyzing biobank-scale data (UKB RAP, All of Us).
• Proficiency in Python and R; Unix familiarity; code documentation and GitHub workflows.

Responsibilities

• Leverage human genetic datasets to identify causal pathways and therapeutic hypotheses.
• Integrate multi-trait genetics, functional annotations, and omics data to elucidate biology.
• Characterize variants and genes in biobank-scale studies to inform targets.

Skills

Statistical genetics

GWAS

RVAS

Biobank data

Python

R

GitHub

Communication

Education

PhD in Statistical Genetics

Tools

UNIX

Python

R

GitHub

UK Biobank RAP

All of Us Researcher Workbench

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 supportpipeline.

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