Genomic AI Scientist – Drug Discovery Data Scientist
Eli Lilly and Company (company site, opens in a new tab)8+ yrsBengaluruOn-siteDirect career pageEst. 40–70 LPA
Posted 3d ago
Reach people at Eli Lilly and Company
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Eli Lilly and Company you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Lead the genomic AI portfolio including language models, multi-omics models, and AI applied to genetic medicines and target selection.
- Mentor and influence bioinformaticians, data scientists, and ML engineers; align stakeholders and collaborate with senior leadership on strategy and roadmap.
- Own and solve the most challenging technical problems in genomic AI, setting scientific direction and representing the team in leadership and governance forums.
Minimum Requirements
- Ph.D. in computational biology, bioinformatics, genomics, computer science, machine learning, or related quantitative field with 5+ years post-Ph.D. experience applying ML to genomic sequence data; or M.S. with 8+ years equivalent experience.
- Hands-on experience building deep learning models on DNA/RNA sequence data using Python and PyTorch or JAX/TensorFlow.
- Proven technical leadership driving direction and cross-team standards in genomic deep learning projects.
- Experience mentoring scientists or engineers and aligning scientific and engineering stakeholders without formal authority.
Ideal Candidate Profile
- Strong background in genomic deep learning and application of DNA/RNA foundation models (e.g., Enformer, DNABERT) for regulatory genomics and genetic medicine design.
- Experienced in integrating multi-omics data and human genetic evidence (GWAS, fine-mapping) to inform target selection strategies.
- Skilled at translating complex technical advances into actionable business and scientific impact at senior leadership levels with demonstrated influence and stakeholder alignment skills.