Sr. Scientist, Statistical Genomics

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

Foresite Labs is a translational R&D team that derives insights from precision measurement and population-scale biology and genetics to address unmet clinical needs. We use human genetics to systematically dissect and understand human disease biology and develop and critically evaluate therapeutic hypotheses. We engage in translational research, transforming basic insights into therapeutic opportunities. Our work supports drug discovery and company formation, and provides the core around which new ideas are realized and incubated. We offer competitive salaries, excellent benefits, a flexible work environment, and the opportunity to learn from top thinkers in various disciplines. Foresite Labs is headquartered in San Francisco and Boston.

Role

We are looking for an applied statistical geneticist to join our team to support the development and testing of therapeutic hypotheses across disease domains. We offer a flexible work environment, a diverse set of projects, and a best-in-class peer group to learn from. This is a great opportunity to tackle a unique set of problems while shaping the future of healthcare. If you’re a statistical geneticist or genetic epidemiologist with interest in using your skills and experience to understand and improve human health, let’s talk.

Responsibilities

• Design, execute, and quality control large-scale human genomic analyses to evaluate therapeutic hypotheses and opportunities across all disease areas.
• Evaluate and design large-scale human genomic discovery collaborations.
• Work in close peer-to-peer collaboration with colleagues throughout Foresite Labs and Foresite Capital to identify opportunities for therapeutic translation.
• Interpret and communicate analysis findings to internal and external stakeholders to support investment decision-making with genomic insights.

Qualifications

Required

• PhD in Human Genetics, Statistical Genetics, Genetic Epidemiology, Biostatistics, or Statistics
• 3-5 years of experience in human genomics research post PhD. 
• A proven track record of genomic discovery in large-scale GWAS and WES/WGS datasets in academia and/or industry.
• Knowledge of the full range of genome-wide and phenome-wide analysis approaches, encompassing genotype and sample quality control, phenotype evaluation, imputation, and approaches for well-calibrated association discovery in the presence of cryptic relatedness, population structure, extreme case-control imbalance, and sparse observations.
• Hands-on experience in the design, execution, and quality control of common and rare variant association analyses in large WES/WGS and GWAS datasets. Familiarity with the full range of single- and aggregated marker methods (e.g. burden, SKAT, VT).
• Experience leveraging a broad range of publicly available databases to annotate data/results or supplement results interpretation from statistical analyses. 
• Extensive experience in the design and execution of meta- and mega-analyses of common, rare, and ultra-rare variants in single- and multi-ancestry collections.
• Experience in polygenic score construction, validation, and application.
• Experience in fine-scale locus dissection and in causal inference from individual-level and summary association data.
• Extensive experience in statistical programming with R, Python, or other programming languages, and with tools for large-scale analyses (e.g. SAIGE, REGENIE, METAL/RAREMETAL, MAGMA/FUMA, Coloc/FastEnloc, 2SMR, etc.).
• Excellent oral and written communication skills. Ability to communicate complex concepts to audiences with a wide range of backgrounds and technical familiarity.
• Ability and desire to work in a fast-paced, interactive, and fluid environment in a multidisciplinary team focused on therapeutically-relevant genomic discovery.

Preferred

• 2+ years of pharmaceutical industry research and development experience.
• Experience in the design, management, and execution of large-scale multi-site collaborative studies and with electronic health record derived phenotypes.
• Experience in statistical computing in distributed computing environments using Scala/Spark framework.

Foresite Labs is an equal opportunity employer. We thrive on diversity and collaboration.