Staff Scientist, Statistical Genetics & Genomics AI

Radical Numerics Inc.; San Francisco (CA); On-site; USD 180,000 - 280,000; Full time

Job summary

Radical Numerics seeks a seasoned statistical genetics professional to turn association data into foundations for models. You will work with GWAS, QTLs, variant annotation, and biobank-scale phenotypes to test how well models understand genetic variation and its relation to traits.

You will design datasets, benchmarks, and analyses with AI engineers and computational biologists to ensure performance and interpretability of biological foundation models.

Qualifications

• PhD in statistical genetics or related field, or substantial industry experience with population-scale genetic data.
• Deep knowledge of GWAS, LD, ancestry, heritability, fine-mapping, QTL mapping, rare variant analysis, polygenic risk, and variant annotation.
• Experience with large genetic resources such as UK Biobank, All of Us, TOPMed, gnomAD, GTEx, FinnGen, ENCODE, or similar datasets.
• Strong Python or R programming skills and ability to build reliable, reproducible scientific analyses.

Responsibilities

• Build and evaluate large-scale genetics resources for model training and assessment.
• Design benchmarks for genetic architecture: LD, ancestry, constraint, polygenicity, pleiotropy, regulatory effects, rare variant burden, cross-population generalization.
• Partner with AI/ML engineers to analyze model behavior on variant effect prediction, disease association, genotype-to-phenotype prediction, and regulatory region interpretation.
• Develop practical standards for genetic data provenance, QC, leakage prevention, population bias assessment, privacy, consent, and responsible use of human genetic data.

Skills

Python or R programming

Strong communication

Curiosity and resilience

Statistical genetics concepts (GWAS,LD

Education

PhD in statistical genetics / related field

Tools

Python

R

Job description

Radical Numerics seeks a seasoned statistical genetics professional to turn association data into foundations for models. You will work with GWAS, QTLs, variant annotation, and biobank-scale phenotypes to test how well models understand genetic variation and its relation to traits.

You will design datasets, benchmarks, and analyses with AI engineers and computational biologists to ensure performance and interpretability of biological foundation models.