Staff Scientist, Statistical Genetics & Genomic Models

radicalnumerics.ai; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Radical Numerics in San Francisco is seeking a Member of Technical Staff focused on statistical genetics to turn genetic association data into a rigorous substrate for biological foundation models. You will work with GWAS, QTLs, variant annotation and biobank-scale phenotypes to ask whether our models understand genetic variation and its relationship to molecular and organismal traits.

This role blends data architecture, methods research and model evaluation in a collaborative team with AI

Qualifications

• PhD in statistical genetics, human genetics, computational biology, biostatistics, or a related field, OR substantial industry experience.
• Strong Python or R programming skills and ability to build reproducible analyses.
• Experience with large genetic resources such as UK Biobank, All of Us, TOPMed, gnomAD, GTEx, FinnGen, ENCODE, or similar datasets.

Responsibilities

• Build and evaluate large-scale statistical genetics resources for model training and assessment.
• Design benchmarks that test whether models capture genetic architecture: LD, ancestry, constraint, polygenicity, pleiotropy, regulatory effects, rare variant burden, and 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

Statistical genetics concepts (GWAS,LD

Communication with engineering teams

Education

PhD in statistical genetics or related field

Tools

Python or R tooling

Job description

Radical Numerics in San Francisco is seeking a Member of Technical Staff focused on statistical genetics to turn genetic association data into a rigorous substrate for biological foundation models. You will work with GWAS, QTLs, variant annotation and biobank-scale phenotypes to ask whether our models understand genetic variation and its relationship to molecular and organismal traits.

This role blends data architecture, methods research and model evaluation in a collaborative team with AI