Senior Scientist II, Statistical Genetics at Cytokinetics in San Francisco, CA

Job description

Cytokinetics is a specialty cardiovascular biopharmaceutical company, building on its over 25 years of pioneering scientific innovations in muscle biology, and advancing a pipeline of potential new medicines for patients suffering from diseases of cardiac muscle dysfunction. We are seeking a highly motivated and skilled statistical geneticist to join our growing team. You will play a critical role in discovering and evaluating therapeutic targets for diseases involving impaired cardiac and skeletal muscle function. You will lead hands-on research using large human cohorts, sequencing data and rich phenotypes to connect genetic variation with disease biology and therapeutic opportunities. This role requires a strong foundation in statistical genetics, applied AI/ML experience, and a passion for translating genetic insights into clinical applications. Responsibilities: Perform common and rare variant association studies and meta-analyses across biobanks (e.g., UK Biobank, All of Us, FinnGen) to discover and replicate associations across diverse ancestries Develop quantitative and longitudinal phenotypes from EHR and imaging (e.g., cardiac MRI, DEXA) using AI/ML methods to better capture disease biology Apply fine-mapping, colocalization, Mendelian randomization and burden methods, including protein language model-based variant prioritization, to identify causal genes and direction of effect Prioritize targets using genetic evidence for efficacy and safety (e.g., PheWAS, allelic series, protective variants) and partner with experimental teams on validation Build reproducible pipelines on research analysis platforms, and present findings internally, at conferences and in publications Qualifications: Ph.D. in Statistical Genetics, Human Genetics, Bioinformatics, Computational Biology or a related field with 4+ years of post-doctoral or industry experience Strong expertise in analyzing large-scale genomic datasets, including GWAS and sequencing data Demonstrated experience translating genetic associations into candidate therapeutic hypotheses or targets Experience with multidimensional and longitudinal data analysis in biology or medicine Proficiency in R and/or Python, with experience applying and critically evaluating statistical and ML methods Track record of independent research (e.g., first-author publications, methods, software) and strong communication skills Preferred Qualifications: Experience with cloud computing platforms (e.g., AWS, Google Cloud) and biobank research analysis platforms (e.g., UKB RAP, All of Us Workbench) Experience with machine learning and deep learning methods, and strong interest in applying these methods to biological problems Previous therapeutic area experience in cardiovascular and neuromuscular disease is strongly desired Experience using AI-assisted coding tools in reproducible workflows You don’t need every item on this list. We hire for judgment, rigor and curiosity. To Apply: Please submit your CV, a cover letter outlining your research experience and interests, and a list of publications. Pay Range: The expected salary range for this position based on the primary location of South San Francisco, CA is $184,500-$215,250. The base pay offered will take into account internal equity and may vary depending on the candidate's geographic region, job-related knowledge, skills, and experience among other factors. Base salary is just one component of our competitive total compensation package, which includes equity in the form of stock options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment. Our employees come from different backgrounds, and we celebrate those differences. We are looking for the best candidates for our open roles, but do not expect applican