Computational Scientist: Human Genetics

The Human Genetics department at the client site is seeking a highly independent computational scientist with a strong hands-on analytical background in genetic epidemiology, statistical genetics, or computational biology, to develop and apply analytical approaches to integrate and interpret genetic, genomic, and clinical data. We are particularly interested in candidates with skill sets that position them to tackle the integration of multiple sources of human biological data, such as whole genome sequencing and single cell RNA-Seq/ATAC-Seq data, including knowledge of emerging multimodal data integration methods.

Responsibilities:

• Collaborate with scientists in Human Genetics department to analyze large datasets of genetic, genomic, and clinical data from internal studies (including our clinical trials and high throughput screens), collaborations with academic and industry partners, and public external data sets
• Develop analytical approaches to integrate and interpret these data, delivering insights into disease biology to propel our translational goals
• Coordinate the intake and preparation of new datasets as they become available for analysis
• Document process, findings, and code
• Present findings to the department and cross-functional collaborators and contribute to publications

Requirements:

• PhD (or Masters with significant experience) in Statistical Genetics, Computational Biology, Bioinformatics, Genetic Epidemiology, or a related field.
• Proficient experience in programming with R and Python.
• Solid knowledge of command line interface (Bash).
• Demonstrated experience in the design, quality control, and performing association analyses (GWAS and Burden tests) for common and rare variants using array- and sequence-based genetic data .
• Experience working with biobank data (examples such as UK Biobank, FinnGen or All of Us).
• Experience working with Electronic Health Records (EHR) or other Real World Data (RWD). Proficient in software engineering practices, including version control using Git and executing parallel computing workflows (e.g., Slurm batch jobs).
• Experience working with workflow management systems such as WDL, Nextflow, or other relevant workflow management languages.
• Prior experience working on Cardiovascular Diseases is a plus.
• Ability to create detailed documentation of work completed as well as analyses performed.
• Curiosity and desire to learn more about human genetics, bioinformatics, and biology.
• Ability to produce high-quality analysis results with minimal supervision. This includes meeting key deadlines and making sensible independent decisions.
• Good communication skills and experience working as part of a team.

Pay Rate Range: $50-63/hr depending on experience