Biophysical/Analytical Scientist – Developability Lead

Eli Lilly and Company; Indianapolis (IN); On-site; USD 129,000 - 211,000; Full time

Benefits offered by this job

Company bonus

401(k) retirement plan

Medical, dental, vision benefits

Pension program

Vacation benefits

Job summary

Eli Lilly and Company is seeking a Biophysical/Analytical Scientist in its BioTDR Lead Optimization group. The role centers on expert biophysical characterization of biotherapeutics to drive developability and candidate selection decisions.

The ideal candidate has deep experience with chromatographic, spectroscopic, scattering, calorimetric, and binding-kinetics methods, plus the ability to collaborate across R&D teams and apply computational modeling when applicable.

Qualifications

• Ph.D. or MS with substantial industry experience in biopharmaceutical analytics.
• Proven deep expertise in biophysical characterization across multiple platforms.
• Experience with complex modalities such as antibody conjugates is preferred.

Responsibilities

• Serve as biophysical/analytical lead for assigned programs, guiding developability and formulation decisions.
• Collaborate with engineers and scientists to use characterization data for SAR and risk assessment.
• Apply diverse biophysical methods across RP-HPLC, aSEC, HILIC, IEX, CE-SDS, cIEF, nanoDSF, CD, and various light scattering/ calorimetric techniques.
• Document procedures and communicate results via presentations and electronic notebooks.

Skills

Biophysical characterization

Binding kinetics platforms

Analytical data interpretation

GenAI productivity

Education

Ph.D. in biochemistry/biotechnology/analytical chemistry or related field

M.S. with 7+ years industry experience

Tools

Biacore/SPR

Octet/BLI

Lab automation

High-throughput platforms

Job description

Eli Lilly and Company is seeking a Biophysical/Analytical Scientist in its BioTDR Lead Optimization group. The role centers on expert biophysical characterization of biotherapeutics to drive developability and candidate selection decisions.

The ideal candidate has deep experience with chromatographic, spectroscopic, scattering, calorimetric, and binding-kinetics methods, plus the ability to collaborate across R&D teams and apply computational modeling when applicable.