To support the data, tools and infrastructure that are critical for enabling clinical research objectives that will inform and accelerate the discovery and development of next generation therapeutics for diabetes, obesity and associated complications at Eli Lilly and Company
Requirements
- Ph.D. in Statistical Genetics, Biostatistics, Computer Science or a related quantitative field
- M.S. in Statistical Genetics, Biostatistics, Computer Science or a related quantitative field with 5+ years of post-degree experience with a focus on genetics and genomics
- Demonstrated accomplishments in genetics and genomics data analysis
- Strong programming abilities to implement and apply statistical genetics principles
- Strong statistics and quantitative background, with experience in AI/ML a plus
- Familiarity with public databases such as UK Biobank, etc.
- Experience with analyzing sequencing data from cohorts with multiple ancestries and complex phenotypes
- Experience applying genetics to therapeutic target discovery or validation workflows
- Experience processing, assessing quality and analyzing multi-omics data, including proteomics data
Responsibilities
- Lead the design and implementation of statistical genetics workflows supporting the analysis and interpretation of genomic studies
- Work with clinicians, statisticians and biologists to develop experimental hypotheses and help design and author computational analysis plans for genomic data and other computational data types
- Analyze WES, WGS and paired omics data and contribute to data interpretation and biological conclusions
- Implement and apply summary-statistics-based statistical methods including finemapping, colocalization, and causal inference to guide drug target discovery and mechanism of action studies
- Help streamlining and automating genetics and genomics workflows and building databases and visualizations using internal and external datasets
- Contribute to scientific and strategy discussions to advance and improve Lilly Genetics
- Communicate experimental data to scientific and management teams
Other
- Excellent communication and presentations skills, capable of conveying domain specific technical information in a clear and thorough manner to interdisciplinary audiences across the organization
- Strategic, self-motivated, and enjoys teamwork within an interdisciplinary and dynamic team environment
- Ph.D. or M.S. degree in a related quantitative field
- 5+ years of post-degree experience with a focus on genetics and genomics
