Genomic AI Scientist - Drug Discovery Data Scientist

Job Summary

The Advanced Intelligence and Research IT team in 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 teams 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

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