Principal/Senior Statistical Geneticist (Hereditary Disease)
Passionate about precision medicine and advancing the healthcare industry?
Recent advancements in genomics and compute technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time.
We are looking for an experienced Statistical Geneticist with strong computational skills and demonstrated experience in genotype-phenotype associations analyzes of complex traits with large scale datasets.
The successful candidate will work on one of society's biggest healthcare problems of how to associate the right therapy at the right time to the right individual.
What You'll Do
- The statistical geneticist will use Tempus’ extensive database of genotype and phenotype data to develop methods and implement analysis that address questions around associating risk of developing a disease, progressing, comorbidities, complications, or risk of therapy failure.
- You will use cutting edge statistical genetic analysis methods to generate insights about human disease and diverse complex traits.
- Integrate and lead hypotheses generation from genetics and diverse datasets by employing sound statistical genetics and computational biology approaches in a pan-therapeutic manner.
- Bring in datasets from external and internal sources to help develop internal resources for various analytical approaches involving genetics.
- Develop and apply innovative statistical genetics approaches to understand and predict complex disease conditions using multi-dimensional –omics and clinical data.
- Develop working prototypes of inference methods for transfer to production software engineers.
- Work collaboratively within a diverse team of Clinical Scientists, Computational Biologists, and Bioengineers.
- Ph.D. in Statistical Genetics, Genetic Epidemiology, Quantitative Genetics, Biostatistics, or related area, or equivalent.
- Programming experience using Python/Jupyter notebooks and/or R/Bioconductor in analyzing large data sets in modern cluster and cloud computing environments.
- Proficiency in biostatistics, linear/non-linear regression models, dimensionality reduction, clustering, and/or bayesian networks methods
- 3+ years experience with approaches for marker-trait and gene-trait association (GWAS, PheWAS, TWAS, QTL mapping, Mendelian randomization, etc), especially as they relate to implications in large multi-ethnic cohorts, and/or polygenic risk score prediction / genomic prediction
- Experience conducting human genetic analyses in admixed populations, including genome-wide and local ancestry analysis.
- Ability to develop, benchmark and apply predictive algorithms to identify novel biomarkers, dissect gene/disease relationships and generate hypotheses
- Knowledge and expertise with whole genome as well as whole exome sequence analysis
- Exposure to and ability to work with data from UK Biobank, DisGeNet, & FinnGen is a plus
- Ability to manage multiple projects in parallel, communicate clearly and effectively and build open and collaborative relationships
- Experience with communicating insights and presenting concepts to a diverse audience
- Self-driven and works well in interdisciplinary teams
We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
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