Principal Scientist, Translational Genetics
Cytokinetics
September 02, 2026
Remote friendly (San Francisco Bay Area)
United States
Clinical Research and Development
Responsibilities:
- Perform GWAS, fine-mapping, and other statistical genetics analyses using large-scale genomic datasets (UK Biobank, All of Us, FinnGen, etc.).
- Analyze and integrate multi-omics (genomics, transcriptomics, proteomics, metabolomics) to identify causal variants/pathways in cardiovascular diseases.
- Develop/apply statistical models to predict disease risk and treatment response from genetic and clinical data.
- Design/evaluate AI/ML for imaging-derived phenotyping (e.g., cardiac MRI, DEXA).
- Conduct Mendelian randomization studies to infer causal relationships between variants and cardiovascular traits.
- Identify/prioritize genetic therapeutic targets; contribute to study design/analysis to validate drug targets and biomarkers.
- Collaborate with experimental biologists and clinicians to translate findings into preclinical/clinical research.
- Build/maintain bioinformatics pipelines for genomic and EHR data; manage/curate large datasets.
- Use/develop analysis and visualization tools (R, Python, PLINK, Hail).
- Present results (conferences, publications), contribute to regulatory docs/grant applications, and maintain organized analysis records.
Qualifications (Required):
- Ph.D. in Statistical Genetics, Human Genetics, Bioinformatics, Computational Biology, or related field.
- 6+ years biotech/pharma experience (or relevant post-doc) with demonstrated impact.
- Strong expertise in analyzing large-scale genomic datasets (GWAS/sequencing); R/Python proficiency and bioinformatics tools.
- Strong analytical/problem-solving, communication/presentation, teamwork, organization/time management, and ability to learn quickly.
Preferred:
- Mendelian randomization and multi-omics integration experience.
- Cardiovascular/cardiometabolic domain experience.
- Cloud platforms (AWS/Google Cloud) and biobank platforms; ML/deep learning; multidimensional/longitudinal data analysis; peer-reviewed publications.
Application: Submit CV, cover letter, and list of publications.
Pay range (U.S.): $202,500–$236,250/year.
- Perform GWAS, fine-mapping, and other statistical genetics analyses using large-scale genomic datasets (UK Biobank, All of Us, FinnGen, etc.).
- Analyze and integrate multi-omics (genomics, transcriptomics, proteomics, metabolomics) to identify causal variants/pathways in cardiovascular diseases.
- Develop/apply statistical models to predict disease risk and treatment response from genetic and clinical data.
- Design/evaluate AI/ML for imaging-derived phenotyping (e.g., cardiac MRI, DEXA).
- Conduct Mendelian randomization studies to infer causal relationships between variants and cardiovascular traits.
- Identify/prioritize genetic therapeutic targets; contribute to study design/analysis to validate drug targets and biomarkers.
- Collaborate with experimental biologists and clinicians to translate findings into preclinical/clinical research.
- Build/maintain bioinformatics pipelines for genomic and EHR data; manage/curate large datasets.
- Use/develop analysis and visualization tools (R, Python, PLINK, Hail).
- Present results (conferences, publications), contribute to regulatory docs/grant applications, and maintain organized analysis records.
Qualifications (Required):
- Ph.D. in Statistical Genetics, Human Genetics, Bioinformatics, Computational Biology, or related field.
- 6+ years biotech/pharma experience (or relevant post-doc) with demonstrated impact.
- Strong expertise in analyzing large-scale genomic datasets (GWAS/sequencing); R/Python proficiency and bioinformatics tools.
- Strong analytical/problem-solving, communication/presentation, teamwork, organization/time management, and ability to learn quickly.
Preferred:
- Mendelian randomization and multi-omics integration experience.
- Cardiovascular/cardiometabolic domain experience.
- Cloud platforms (AWS/Google Cloud) and biobank platforms; ML/deep learning; multidimensional/longitudinal data analysis; peer-reviewed publications.
Application: Submit CV, cover letter, and list of publications.
Pay range (U.S.): $202,500–$236,250/year.