Genomic AI Scientist – Drug Discovery Data Scientist

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.
About Lilly
At Lilly, everything we do starts with patients. We unite caring with discovery to make life better for people around the world. Headquartered in Indianapolis, Indiana, our global team of over 50,000 employees work with urgency and purpose to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. We bring our best to this work because people depend on it. If you're driven by purpose and determined to make a meaningful difference for patients, we invite you to bring your skill and your commitment to Lilly.
About Technology@Lilly
At Lilly, technology is not a support function. It is how a global medicine company operates, innovates, and delivers. Lilly in Bengaluru builds the capabilities that make this possible, cloud platforms, AI systems, and automation at enterprise scale, all in service of a purpose that makes this technology work genuinely distinctive, from advancing drug discovery to enabling connected clinical trials to keeping a global medicine company running at the standard patients deserve.
Genomic AI Scientist – Drug Discovery Data Scientist
About the Role
The Advanced Intelligence and Research IT team at Tech@Lilly Bengaluru is hiring a Genomic AI Scientist to lead our genomic AI portfolio: genomic language models, multi-omics foundation models, and AI for genetic medicines and target selection. About 30% of the role is leadership: mentoring bioinformaticians, data scientists, and ML engineers, aligning stakeholders across science and engineering, and partnering with senior leadership on strategy and prioritization. The other 70% is hands-on ownership of the portfolio's hardest technical problems.
This role carries no formal people-management responsibility; your impact comes through influence, mentorship, and technical credibility. You will set the scientific direction for how genomic AI is built and applied across the team, be the team's accountable voice in leadership and governance forums and grow the technical capability of the people around you.
Leadership, Mentoring & Stakeholder Alignment (~30%) Define and own the genomic AI technical roadmap, setting the portfolio's scientific direction and securing leadership agreement on priorities and sequencing
Mentor and develop bioinformaticians, data scientists, and ML engineers across the genomic AI squads through coaching, design and code reviews, and career-growth conversations
Drive alignment across genetic medicine scientists, ML engineering teams, and therapeutic-area partners on roadmap sequencing, trade-offs, and shared success criteria
Own genomic AI readouts in leadership, portfolio, and governance forums, translating technical progress into clear business and scientific impact
Build a culture of scientific rigor and knowledge sharing through technical reviews, demos, and documentation that raise the bar across the team
Hands-on Technical Ownership (~70%)
Genomic Language Models & Genetic Medicines Lead development, fine-tuning, and benchmarking of DNA/RNA foundation models for sequence-to-expression, variant effect, and regulatory genomics prediction, including ChIP-seq binding modeling
Lead the use of generative and predictive sequence models for AAV promoter design, including cross-model scoring and ensemble ranking of candidates
Direct on- and off-target prediction for genetic medicines across DNA, RNA, protein, and phenotype layers
Partner with genetic medicine scientists to turn model output into design recommendations and go/no-go decisions
Target Selection & Multi-Omics Integration Integrate genomic model output with human genetic evidence (GWAS, fine-mapping, colocalization) to sharpen target selection
Set the team's modeling strategy for multi-omics foundation models spanning DNA, RNA, protein, and clinical phenotype, including how these data types are integrated and evaluated
Ensure model-based assessments are reproducible, defensible, and candid about what the evidence does and does not support
Basic Requirements
Hands-on experience building deep learning models on DNA/RNA sequence (e.g., sequence-to-expression, variant effect, regulatory genomics) with strong Python and PyTorch or JAX/TensorFlow skills
Ph.D. in computational biology, bioinformatics, genomics, computer science, machine learning, or a related quantitative field with 5+ years of post-Ph.D. experience applying machine learning to genomic sequence data; or M.S. in a related field with 8+ years of equivalent experience
Proven technical leadership: setting direction and driving cross-team standards on genomic deep learning projects
Demonstrated record of mentoring scientists or engineers and aligning scientific, engineering, and leadership stakeholders around shared priorities without formal authority
Experience presenting technical strategy and outcomes to senior scientific or engineering leadership, translating technical progress into clear business and scientific impact
Preferred Qualifications
Deep expertise with DNA/RNA foundation models (e.g., Enformer, DNABERT, Nucleotide Transformer, Evo, HyenaDNA) and their fine-tuning or benchmarking
Experience with generative sequence design for regulatory elements, promoters, or AAV/gene-therapy vector components
Familiarity with ChIP-seq, ATAC-seq, or other functional genomics data, and experience integrating multi-omics data spanning DNA, RNA, protein, and clinical phenotype
Experience working with human genetic evidence (e.g., GWAS, fine-mapping, colocalization) and applying it to target selection
Experience leading technical steering groups, cross-functional working sessions, or governance reviews
Track record of growing junior talent into independent contributors or technical leads
Comfort with GPU/HPC environments, large-scale training workflows, and reproducible ML practices such as experiment tracking, version control, and containerized pipelines
Peer-reviewed publications or open-source contributions in genomic deep learning or functional genomics
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