All Of Our Groups

Healthcare

Director,DiscoveryBioinformaticsOncology

$194–339k San Francisco, California, United States FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Director candidates.

The Brief

“Director, Discovery Bioinformatics Oncology at All Of Our Groups. Skills: AI/ML strategy, Machine learning, Deep learning, LLM applications. Innovate and execute AI/ML strategy. Build models for target ID/validation”

What You'll Achieve.

Accelerate target identification; Accelerate target validation; Accelerate protein design; Accelerate antibody design; Accelerate data integration; Transform data into hypotheses; Design in silico-to-in vitro loops; Deliver decision-quality insights; Shape discovery roadmap; Scale across programs

Industry & Context.

Healthcare
Problems you'll solve

Data-driven decision making

What They're Looking For.

Must Have

PhD or MS in STEM field, 5+ years industry experience delivering ML solutions

Nice to Have

Experience leading teams, Experience applying deep learning to biological problems, Experience with Hugging Face, Track record building LLM applications, Software engineering skills, Evidence of scientific leadership

What You'll Do.

Innovate and execute AI/ML strategy

Build models for target ID/validation

Build models for protein design

Build models for biomarker discovery

Establish LLM workflows for knowledge mining

Develop data integration platforms

Integrate imaging data

Integrate knowledge graphs

Drive ontology harmonization

Drive model registries

Advance computational protein design

Leverage sequence models

Leverage graph methods

Operationalize active-learning loops

Lead antibody-siRNA conjugate design

Partner with Biology/Chemistry

Represent computational strategy

Deliver scalable ML systems

Institute model governance

Perform omics analysis

Perform statistical analysis

Ensure data integrity

Ensure FAIR practices

How You'll Work.

Team & Collaboration

Cross-functional teams; Steering committees

Communication Scope

External publish; External present

Full Job Description

At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world. **Job Summary:** Lead the **AI/ML innovation & deployment** for oncology discovery. This role will architect and operationalize **state‑of‑the‑art machine learning** —including deep learning, foundation models, and LLM‑powered applications—to **accelerate** target identification & validation, protein and antibody design, and multimodal data integration across our discovery pipeline. Partnering closely with biology, chemistry, translational sciences and data hub, you’ll transform heterogeneous molecular and phenotypic data into actionable hypotheses, design in silico–to–in vitro loops, and deliver decision‑quality insights that shape our discovery roadmap. This role also steers platformization efforts for in silico design & advancement of antibody, XDC development and next‑generation data products that scale across programs. **Job Responsibilities:** * **Innovate and execute the AI/ML strategy for discovery.** Build a portfolio of models for target ID/validation, structure‑ and sequence‑based protein design (e.g., antibodies, conjugates), mode‑of‑action inference, and biomarker discovery. Establish retrieval‑augmented and agentic LLM workflows for knowledge mining (literature, patents, internal reports) and protocol/screen design assistance. * **Develop next‑gen data integration platforms.** Integrate bulk & single‑cell transcriptomics, WES/WGS, proteomics, CRISPR screen data, imaging, functional readouts, and real‑world knowle

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