Principal Scientist, Translational Genetics
Cytokinetics
August 11, 2026
Remote friendly (San Francisco Bay Area)
United States
Clinical Research and Development
Responsibilities
- Perform genome-wide association studies (GWAS), fine-mapping, and other statistical genetics analyses using large-scale genomic datasets.
- Analyze and integrate multi-omics data (genomics, transcriptomics, proteomics, metabolomics) to identify causal variants and pathways associated with cardiovascular diseases.
- Develop and apply statistical models to predict disease risk and treatment response based on genetic and clinical data.
- Design and evaluate AI/ML methods for large-scale imaging-derived phenotyping (e.g., cardiac MRI, DEXA).
- Conduct Mendelian randomization studies to infer causal relationships between genetic variants and cardiovascular traits.
- Identify and prioritize genetic targets for therapeutic intervention based on statistical and functional evidence.
- Contribute to the design and analysis of genetic studies to validate drug targets and biomarkers.
- Collaborate with experimental biologists and clinicians to translate genetic findings into preclinical and clinical research.
- Develop and maintain bioinformatics pipelines for processing and analyzing genomic and EHR data.
- Manage and curate large-scale genetic and clinical datasets.
- Utilize and develop statistical software/tools for data analysis and visualization (e.g., R, Python, PLINK, Hail).
- Collaborate cross-functionally; present findings; contribute to regulatory documents and grant applications; maintain detailed analysis records.
Qualifications
- Ph.D. in Statistical Genetics, Human Genetics, Bioinformatics, Computational Biology, or related field.
- 6+ years of biotech/pharma experience (or relevant post-doctoral experience) with demonstrated impact.
- Strong expertise in analyzing large-scale genomic datasets (GWAS, sequencing).
- Proficiency in R/Python and bioinformatics tools.
- Experience with Mendelian randomization and multi-omics integration (highly desirable).
- Cardiovascular/cardiometabolic therapeutic domain experience (strongly preferred).
Preferred
- Cloud computing (AWS/Google Cloud) and biobank platforms (DNAnexus RAP, All of Us Workbench).
- Machine/deep learning experience; interest applying to biological problems.
- Multidimensional and longitudinal data analysis experience.
- Peer-reviewed publications in statistical genetics and cardiovascular disease.
Application
- Submit your CV, a cover letter, and a list of publications.
- Perform genome-wide association studies (GWAS), fine-mapping, and other statistical genetics analyses using large-scale genomic datasets.
- Analyze and integrate multi-omics data (genomics, transcriptomics, proteomics, metabolomics) to identify causal variants and pathways associated with cardiovascular diseases.
- Develop and apply statistical models to predict disease risk and treatment response based on genetic and clinical data.
- Design and evaluate AI/ML methods for large-scale imaging-derived phenotyping (e.g., cardiac MRI, DEXA).
- Conduct Mendelian randomization studies to infer causal relationships between genetic variants and cardiovascular traits.
- Identify and prioritize genetic targets for therapeutic intervention based on statistical and functional evidence.
- Contribute to the design and analysis of genetic studies to validate drug targets and biomarkers.
- Collaborate with experimental biologists and clinicians to translate genetic findings into preclinical and clinical research.
- Develop and maintain bioinformatics pipelines for processing and analyzing genomic and EHR data.
- Manage and curate large-scale genetic and clinical datasets.
- Utilize and develop statistical software/tools for data analysis and visualization (e.g., R, Python, PLINK, Hail).
- Collaborate cross-functionally; present findings; contribute to regulatory documents and grant applications; maintain detailed analysis records.
Qualifications
- Ph.D. in Statistical Genetics, Human Genetics, Bioinformatics, Computational Biology, or related field.
- 6+ years of biotech/pharma experience (or relevant post-doctoral experience) with demonstrated impact.
- Strong expertise in analyzing large-scale genomic datasets (GWAS, sequencing).
- Proficiency in R/Python and bioinformatics tools.
- Experience with Mendelian randomization and multi-omics integration (highly desirable).
- Cardiovascular/cardiometabolic therapeutic domain experience (strongly preferred).
Preferred
- Cloud computing (AWS/Google Cloud) and biobank platforms (DNAnexus RAP, All of Us Workbench).
- Machine/deep learning experience; interest applying to biological problems.
- Multidimensional and longitudinal data analysis experience.
- Peer-reviewed publications in statistical genetics and cardiovascular disease.
Application
- Submit your CV, a cover letter, and a list of publications.