Director, Generative Design Validation & Learning

Lilly; San Diego (CA); On-site; USD 194,000 - 339,000; Full time

Benefits offered by this job

401(k) plan

Pension

Medical, dental, vision benefits

Well-being benefits

Job summary

Lilly Biotechnology Center in San Diego is expanding its generative design platform to create de novo biologics with improved activity, safety, and developability. The Director of Design Validation & Learning will lead validation, active learning, and connect computational predictions with experimental data, driving model improvements and experimental campaigns.

You will oversee a team of scientists, establish benchmarks, and collaborate across DMTA, automation, and protein engineering to

Qualifications

• Ph.D. in computational biology, biophysics, computational chemistry, bioengineering, chemical engineering, physics, statistics, or a related STEM discipline with 3+ years of relevant experience; or an M.S. with 6+ years; or a B.S. with 8+ years of relevant experience.
• Strong Python and applied statistical skills, including calibration, uncertainty analysis, and quantitative evaluation.

Responsibilities

• Model evaluation and benchmarking.
• Prospective validation and closed-loop learning.
• Model calibration against experimental data.
• Physics-based computation within the design pipeline.
• Purposeful data generation and active learning.
• Partner with therapeutic discovery teams.
• Lead a high-performing team and provide technical leadership.
• Drive cross-functional learning.
• Scientific influence.

Skills

Python

Applied statistics

Leadership

Experimental collaboration

Education

Ph.D. in computational biology or related field

MS in computational biology or related field

BS in STEM with relevant experience

Tools

Molecular dynamics

TensorFlow/PyTorch

Job description

Lilly Biotechnology Center in San Diego is expanding its generative design platform to create de novo biologics with improved activity, safety, and developability. The Director of Design Validation & Learning will lead validation, active learning, and connect computational predictions with experimental data, driving model improvements and experimental campaigns.

You will oversee a team of scientists, establish benchmarks, and collaborate across DMTA, automation, and protein engineering to