Eli Lilly and Company's Diabetes, Obesity and Complications Therapeutic Area (DOCTA) is seeking a computational scientist to drive therapeutic discovery in metabolic diseases by analyzing multi-omics data and unraveling the complex biology of obesity, adipose tissue function, and metabolic dysregulation.
Requirements
- Experience with scalable cloud computing platforms (e.g. Databricks, AWS) and big data analytics frameworks
- Experience with containerized technologies (e.g. Docker) for computational reproducibility
- Strong track record of execution of computational biology and/or bioinformatics-based projects, potentially including RNA-seq, metabolomics, multi-omics, human genetics, proteomics, AI/ML, and other related research modalities
- Expertise in programming languages including R and Python and experience with workflow management systems suck as Nextflow
- Experience with bioinformatics tools (e.g., IPA, GO, GSEA, KEGG, Bioconductor) and publicly available data resources (e.g., Gtex, UKBB)
- Experience developing production-grade bioinformatics pipelines, including working with standardized workflow tools
- Experience with implementation and maintenance of industry-standard documentation practices including Git, Confluence, JIRA, or equivalent
Responsibilities
- Design and complete studies using omics-related data sources, including RNA-seq, spatial transcriptomics, single-cell omics, proteomics, functional genomics, metabolomics, and more.
- Integrate standard analytical approaches as well as emerging AI/ML models to answer scientific questions of high-dimensional data
- Partner with discovery statistics team to create novel analytical frameworks for high-throughput studies and develop specialized methodologies to enhance insights from small-sample datasets.
- Perform ad-hoc bioinformatics analyses and data visualizations as needed
- Work collaboratively with other Data Sciences and Computational Biology (DSCB) scientists to develop innovative, best in class computational workflows and data repositories for advanced analyses
- Engage in code and documentation review within the team and across other teams within the DSCB team
- Adhere to industry-standard best practices for scientific project documentation
Other
- PhD or equivalent in Computational Biology, Bioinformatics, Biomedical Informatics, or related field with 2+ years of experience post-PhD in relevant disease area.
- Prior industry experience
- Knowledge of human genetics required: direct experience working with human genetics and data preferred
- Prior experience and deep expertise in obesity and related areas, preferably with data from preclinical models, patient cohorts or cell lines
- Ability to prioritize and manage multiple competing priorities within a fast-paced environment
