Principal Scientist, Biophysics & Laboratory Automation

Johnson & Johnson•Spring House, PA

1d•Onsite

About The Position

Johnson & Johnson Innovative Medicine is seeking an accomplished Principal Scientist to join Cell Engineering and Analytical Sciences (CEAS), within the Biophysics & Separations team. The role combines deep expertise in protein characterization and separation sciences with the technical aptitude to translate scientific workflows into automated, digitally enabled solutions. The scientist will lead biophysics automation efforts in coordination with CEAS automation and data science teams and collaborate with Mass Spectrometry, Cell Line Development, and Process Development to connect scientific workflows and advance a shared vision for a self-driving lab. The position will help build an integrated characterization environment in which experiments, instruments, data, and analytics work together. By increasing automation and data digitalization, the role will support faster evaluation of molecules and cell lines, earlier identification of product quality and manufacturing risks, and more informed scientific decisions in biologics development. The Principal Scientist will develop automated workflows for biophysical and protein characterization assays; execute and interpret characterization experiments; apply advanced analytics to characterization data; and help make workflows usable at greater sample volumes and molecular complexity. The role also provides scientific leadership through mentoring and contributions to cross-disciplinary and external collaborations, presentations, publications, and intellectual property.

Requirements

Ph.D. in Biophysics, Biochemistry, Analytical Chemistry, Bioengineering, Biotechnology, or a related scientific or engineering discipline.
At least 4 years of demonstrated biopharmaceutical, biotechnology, or related industry experience.
Deep expertise in protein therapeutics characterization and biophysical or separation methods, including relevant experience with CE-SDS, cIEF, SEC-based separations, chromatography, or related techniques.
Ability to independently prepare samples, execute and troubleshoot analytical methods, assess data quality, and interpret results.
Strong understanding of protein quality attributes, including aggregation, fragmentation, charge heterogeneity, and stability, and their implications for molecule and cell line development.
Ability to design complex experiments, interpret multidimensional datasets, and translate findings into actionable scientific recommendations.
Strong communication, collaboration, and scientific leadership skills, including presenting results and recommendations to teams and at cross-departmental meetings, and tailoring technical detail to the audience.
Demonstrated technical problem-solving aptitude and motivation to take ownership of biophysics automation and data digitalization in partnership with established automation and data science teams.
Willingness and ability to occasionally work outside regular hours to troubleshoot and support ongoing automated experiments.

Nice To Haves

Laboratory automation experience, including liquid handling, robotics, and integration of analytical instruments with automated sample preparation.
Experience with workflow scheduling and orchestration, including development of high-throughput or unattended analytical workflows.
Scientific programming or scripting, such as Python, to support automated data processing, statistical analysis, visualization, and reporting.
Experience with data digitalization and laboratory informatics, including structured capture, standardization, and integration of analytical data across instruments and systems.
Application of AI/ML to scientific data, or use of agentic AI tools for programming, analysis, and workflow development.

Responsibilities

Lead the design, development, and implementation of automated workflows for biophysical and protein characterization assays.
Connect sample preparation, instruments, software, and data processing, using scientific programming and agentic AI where appropriate.
Automate established platforms such as CE-SDS, cIEF, and SEC-UV, and expand automation to new characterization technologies.
Connect experimental execution with automated data capture, processing, analysis, visualization, and reporting, and help establish infrastructure and practices for increasingly autonomous experimentation and scientific decision support.
Design experiments, prepare samples, execute and troubleshoot biophysical characterization methods, assess data quality, and interpret results.
Characterize product quality, molecular heterogeneity, aggregation, fragmentation, and charge variants to inform molecule and cell line selection and identify development and manufacturing risks.
Adapt techniques to complex modalities such as bispecific and trispecific antibodies.
Integrate biophysical characterization results with other cell line development information and apply advanced analytics, including machine learning and AI-enabled approaches, to identify trends, anomalies, and molecule-specific risks.
Simplify routine biophysical workflows and improve usability to support increasing sample volumes and molecular complexity without proportional increases in manual effort.
Evaluate emerging analytical, automation, and digital technologies and partner with digital, data science, and automation teams to advance the self-driving lab vision.
Mentor colleagues in biophysical characterization and increasingly automated, data-driven workflows.
Contribute to cross-disciplinary and external collaborations, presentations, publications, and intellectual property.
Occasionally work outside regular hours to troubleshoot and support ongoing automated experiments.