Principal Scientist, Translational Genetics

Cytokinetics, Inc.; United States; On-site; USD 202,500 - 236,250; Full time

Job summary

Cytokinetics, Inc. is seeking a skilled professional to perform genome-wide association studies and analyze multi-omics data. A Ph.D. in Statistical Genetics or a related field is required, alongside 6+ years of relevant experience.

Responsibilities include developing predictive models, managing genomic datasets, and collaborating with teams to translate findings into research. The position offers a competitive salary range of $202,500.00 - $236,250.00 per year and opportunities for contributions in regulatory documents and publications.

Qualifications

• 6+ years of experience in the biotech or pharmaceutical industry.
• Experience with large-scale genomic datasets, including GWAS and sequencing data.
• Strong analytical and problem-solving skills.

Responsibilities

• Perform genome-wide association studies (GWAS) and statistical genetics analyses.
• Analyze and integrate multi-omics data to identify causal variants.
• Develop statistical models for disease risk prediction.

Skills

Statistical genetics analysis

Multi-omics data integration

Statistical programming (R, Python)

Mendelian randomization

Analytical skills

Communication skills

Education

Ph.D. in Statistical Genetics, Human Genetics, or related field

Tools

R

Python

PLINK

Hail

Job description

Cytokinetics is a specialty cardiovascular biopharmaceutical company building on over 25 years of pioneering scientific innovations in muscle biology, advancing a pipeline of potential new medicines for patients suffering from diseases of cardiac muscle dysfunction.

Responsibilities

• Perform genome‑wide association studies (GWAS), fine‑mapping, and other statistical genetics analyses using large‑scale genomic datasets (e.g., UK Biobank, All of Us, FinnGen).
• 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 electronic health record (EHR) data.
• Manage and curate large‑scale genetic and clinical datasets.
• Utilize and develop statistical software and tools for data analysis and visualization (e.g., R, Python, PLINK, Hail).
• Collaborate with cross‑functional teams, including biologists, clinicians, and computational scientists.
• Present research findings at internal meetings, scientific conferences, and in peer‑reviewed publications.
• Contribute to preparation of regulatory documents and grant applications.
• Maintain detailed and organized records of all analyses.

Qualifications

• Ph.D. in Statistical Genetics, Human Genetics, Bioinformatics, Computational Biology or related field.
• 6+ years of experience in the biotech or pharmaceutical industry (or relevant post‑doctoral experience) with demonstrated impact on project progression.
• Strong expertise in analyzing large‑scale genomic datasets, including GWAS and sequencing data.
• Proficiency in statistical programming languages (R, Python) and bioinformatics tools.
• Experience with Mendelian randomization and multi‑omics data integration is highly desirable.
• Prior experience in the cardiovascular/cardiometabolic therapeutic domain is strongly preferred.
• Strong analytical and problem‑solving skills.
• Excellent communication and presentation skills.
• Ability to work independently and as part of a team.
• Strong organizational and time‑management skills.
• Ability to learn new skills quickly.

Preferred Qualifications

• Experience with cloud computing platforms (AWS, Google Cloud) and biobank research analysis platforms (DNAnexus RAP, All of Us Workbench).
• Experience with machine learning and deep learning methods, with a strong interest in applying these methods to biological problems.
• Experience with multidimensional and longitudinal data analysis in biology or medicine.
• Publications in peer‑reviewed journals related to statistical genetics and cardiovascular disease.

Pay Range: In the U.S., the hiring pay range for fully qualified candidates is $202,500.00 - $236,250.00 per year.

Cytokinetics is an Equal Opportunity Employer.