Eli Lilly is seeking a computational biologist/data scientist to join their emerging Lilly Genomic Sciences team to help shape the computational vision of the team and contribute to research projects across multiple therapeutic areas and data modalities. The role involves collaborating with experimental scientists, interpreting results, and troubleshooting experimental genomics efforts.
Requirements
- extensive experience analyzing and integrating human genetics and functional genomics data
- deep knowledge of gene regulation and the non-coding genome
- Strong background in analyzing NGS–based screening data (e.g. MPRA, STARR-seq, CRISPR regulatory element screening, deep mutational scanning, etc.)
- Strong background in analyzing functional annotation data (e.g. ChIP-seq, ATAC-seq, Hi-C, etc.)
- Strong background in analyzing single cell genomics data
- Strong background in analyzing statistical genetics data (e.g. UKBB, fine-mapping, GWAS)
- Deep knowledge of statistics, genomics, and genomics data integration.
Responsibilities
- Establish and implement data analysis strategies for the integration and interpretation of large-scale functional genomics studies
- Build robust reproducible analysis pipelines for routine use by the team and for deployment on shared cloud computing resources
- Provide computational insights to drive successful troubleshooting of experimental genomics efforts
- Intellectually contribute to the development of a vibrant team of genome scientists who are fiercely dedicated to the application of state-of-the-art genomics approaches to pharmaceutical innovation
- Collaborate with statisticians and other data scientists to drive scientific innovation and team success
- Present scientific findings and progress to internal and external stakeholders, contributing to publications, patents, and conference presentations
- While not the primary focus, this role may involve the creation of some experimental genomics data.
Other
- thought leader with excellent scientific communication skills
- Mentor, train, and professionally develop other scientists and staff within the team
- A significant history and proven track record of high performance on highly collaborative interdisciplinary functional genomics teams.
- Ability to clearly communicate scientific material, efficiently manage time, and work collaboratively
- Cross-training in experimental biology strongly preferred
