Director, De Novo Biologics Design Validation

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

Benefits offered by this job

401(k) plan

Well-being benefits

Paid time off

Job summary

Lilly Biotechnology Center in San Diego hosts a Generative Design platform focused on de novo biologics design. The Director of Design Validation & Learning will lead validation and continuous learning, linking computational predictions with experimental results to strengthen models and molecular design.

You will drive prospective validation studies, establish benchmarks, and advance physics-based computation within the design pipeline, guiding a high-performing team across multiple disciplines.

Qualifications

• Ph.D. in computational biology, biophysics, computational chemistry, bioengineering, chemical engineering, physics, statistics or related STEM with 3+ years of experience; or an MS with 6+ years; or a BS with 8+ years of experience.
• Proven track record translating computational predictions into scientific decisions.
• Strong Python and applied statistics, including calibration and uncertainty analysis.

Responsibilities

• Lead validation and learning for de novo biologics design capabilities across model development, design, and experimental work.
• Design prospective validation studies and benchmarks; interpret results to improve models and designs.
• Coordinate cross-functional work with engineering, automation, and protein design teams to enable iterative learning.

Skills

Python

Applied statistics

Calibration

Uncertainty analysis

Quantitative evaluation

Scientific leadership

Education

Ph.D. in computational biology or related field

M.S. in a STEM field

B.S. with 8+ years relevant experience

Tools

Molecular dynamics

Structure-based modeling

Energy-based methods

Job description

Lilly Biotechnology Center in San Diego hosts a Generative Design platform focused on de novo biologics design. The Director of Design Validation & Learning will lead validation and continuous learning, linking computational predictions with experimental results to strengthen models and molecular design.

You will drive prospective validation studies, establish benchmarks, and advance physics-based computation within the design pipeline, guiding a high-performing team across multiple disciplines.