Computational Biologist – Obesity Research

Role Summary

Computational Biologist – Obesity Research at Lilly. The role emphasizes applying computational and AI/ML approaches to analyze multi-omics data to understand obesity pathophysiology, adipose tissue biology, and metabolic regulation, with cross-functional collaboration to drive therapeutic discovery in metabolic diseases.

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.
• Stay up-to-date on current technical and scientific advances through deep understanding of the literature and attendance of relevant technical and disease-related conferences
• Clearly communicate complex results to cross-functional partners in a prompt and transparent manner
• 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

Qualifications

• 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 is preferred
• 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
• Knowledge of human genetics required; direct experience working with human genetics and data preferred
• 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
• Prior experience and deep expertise in obesity and related areas, preferably with data from preclinical models, patient cohorts or cell lines
• Expertise in programming languages including R and Python and experience with workflow management systems such as Nextflow
• Experience working 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
• Ability to prioritize and manage multiple competing priorities within a fast-paced environment
• The ability to communicate complex scientific and computational concepts to non-computational and non-scientist audiences
• Ability to represent the DOCTA DSCB team internally and externally
• Strongly team-oriented thinking mentality