Principal Scientist, Biophysics & Laboratory Automation

Johnson & Johnson Co.; Spring House (PA); On-site; USD 150,000 - 210,000; Full time

Job summary

Johnson & Johnson is seeking a Principal Scientist, Biophysics & Laboratory Automation within the Innovative Medicine division to advance automated, connected characterization for CEAS. You will lead BP automation, connect workflows across labs, and translate complex data into actionable scientific recommendations.

In this role you will mentor colleagues, collaborate with data science and automation teams, and help scale self-driving lab capabilities to accelerate speed, quality, and

Qualifications

• Ph.D. in Biophysics, Biochemistry, Analytical Chemistry, Bioengineering, Biotechnology, or related field.
• 4+ years of industry experience in biopharmaceuticals or biotechnology.
• Deep expertise in protein therapeutics characterization and biophysical/separation methods.

Responsibilities

• Lead automated workflows for biophysical and protein characterization assays.
• Develop CE-SDS, cIEF, SEC-UV workflows and expand to new technologies.
• Drive data digitalization: automate data capture, processing, analysis, visualization and reporting.
• Design experiments, prepare samples, execute methods, and troubleshoot data.
• Characterize product quality and heterogeneity to inform development risks.
• Turn BP results into actionable scientific recommendations and collaborate with CLD and Process Development.
• Mentor colleagues and provide scientific leadership across CEAS.
• Advance self-driving lab capabilities with automation and AI approaches.

Skills

Analytical Reasoning

Biophysics

Biochemistry

Biotechnology

Protein characterization

Drug Discovery

Data interpretation

Leadership

Tools

CE-SDS

cIEF

SEC-UV

Chromatography

Laboratory Automation

Python

Job description

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com.

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function:

Discovery & Pre-Clinical/Clinical Development

Job Sub Function:

Biotherapeutics R&D

Job Category:

Scientific/Technology

All Job Posting Locations:

Spring House, Pennsylvania, United States of America

Job Description:

About Innovative Medicine

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow. Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.

Learn more at https://www.jnj.com/innovative-medicine

We are searching for the best talent for Principal Scientist, Biophysics & Laboratory Automation.

Purpose:

Johnson & Johnson Innovative Medicine is seeking an accomplished and highly motivated Principal Scientist, Biophysics & Laboratory Automation to join Cell Engineering and Analytical Sciences (CEAS).

You will bring deep expertise in protein characterization and separation sciences, together with the technical aptitude and motivation to translate scientific workflows into automated, digitally enabled solutions.

Working within the Biophysics & Separations team, you will lead biophysics automation efforts in close coordination with the existing CEAS automation and data science teams. Building on established infrastructure, you will develop proficiency in the group's platforms and approaches and collaborate with Mass Spectrometry, Cell Line Development (CLD), and Process Development to connect scientific workflows and advance a shared vision for a self-driving lab.

As therapeutic proteins become more complex, development requires deeper molecular understanding at greater speed and scale. Through data digitalization-connecting experiments, instruments, data, and analytics, not simply automating individual assays-you will enable faster evaluation of molecules and cell lines, earlier identification of product quality and manufacturing risks, and more informed scientific decisions. Your work will help advance an automated, connected characterization environment that supports Speed to Clinic.

Why This Role Matters:

The future of biologics development will require us to generate deeper molecular understanding while simultaneously increasing speed, scale, and efficiency.

This role provides an opportunity to help build that future.

Rather than automating individual assays in isolation, you will help create an integrated characterization environment in which experiments, instruments, data, and analytics work together. These capabilities can enable faster evaluation of molecules during Cell Line Development, earlier identification of potential manufacturing challenges, and more rapid scientific decision-making.

Your work will help transform biophysical and analytical characterization from a series of largely manual workflows into an increasingly automated, connected, and intelligent laboratory capability, supporting CEAS and the broader organization in advancing innovative biologics from development toward the clinic.

You will be responsible for:

• Lead the design, development, and implementation of automated workflows for biophysical and protein characterization assays. Drawing on deep BP expertise and working with the existing automation and data science teams, build connected workflows that make BP characterization faster, more reproducible, and easier for scientists to use.
• Build practical automated workflows: connect sample preparation, instruments, software, and data processing, leveraging scientific programming and agentic AI to develop solutions. Lead the design, development, and implementation of automated workflows for biophysical and protein characterization assays. Automate established platforms such as CE-SDS, cIEF, SEC-UV, and related separation and biophysical methods while expanding automation to new characterization technologies.
• Drive data digitalization: connect experimental execution with automated data capture, processing, analysis, visualization, and reporting, and help establish the infrastructure and ways of working for increasingly autonomous experimentation and scientific decision support.
• Execute biophysical characterization from design through interpretation: design experiments, prepare samples, execute and troubleshoot methods-including CE-SDS, cIEF, and SEC-UV-and evaluate data quality and interpret results.
• Support cell line and molecule development: characterize product quality, molecular heterogeneity, aggregation, fragmentation, and charge variants to inform selection and identify development and manufacturing risks. Adapt techniques to complex modalities such as bi and tri-specific antibodies.
• Turn characterization data into scientific recommendations: integrate BP results with other CLD information and apply advanced analytics, including machine learning and AI-enabled approaches, to identify trends, anomalies, and molecule-specific risks.
• Make BP workflows easier for scientists: simplify routine work, improve usability, and support increasing sample volumes and molecular complexity without proportional increases in manual effort.
• Advance characterization capabilities: evaluate emerging analytical, automation, and digital technologies that address scientific needs and partner with digital, data science, and automation teams to advance the shared self-driving lab vision.
• Provide scientific leadership: mentor colleagues in biophysical characterization and increasingly automated, data-driven workflows; contribute to cross-disciplinary and external collaborations, presentations, publications, and intellectual property.

Qualifications / Requirements:

Required

• Ph.D. in Biophysics, Biochemistry, Analytical Chemistry, Bioengineering, Biotechnology, or a related scientific or engineering discipline, with 4+ years experience of demonstrated biopharmaceutical, biotechnology, or related industry experience.
• Deep expertise in protein therapeutics characterization and biophysical/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 within the team and at cross-departmental meetings, tailoring technical detail to the audience.
• Demonstrated technical problem-solving aptitude and motivation to take ownership of BP 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.

Preferred

Laboratory Automation

• Laboratory automation, including liquid handling, robotics, and integration of analytical instruments with automated sample preparation.
• Workflow scheduling and orchestration, including development of high-throughput or unattended analytical workflows.

Data Digitalization

• Scientific programming or scripting, such as Python, supporting automated data processing, statistical analysis, visualization, and reporting.
• 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 agentic AI tools to programming, analysis, and workflow development.

Johnson & Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected under federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act. Johnson & Johnson is committed to providing an interview process that is inclusive of our applicants' needs. If you are an individual with a disability and would like to request an accommodation, external applicants please contact us via https://www.jnj.com/contact-us/careers , internal employees contact AskGS to be directed to your accommodation resource.

#LI-Onsite

Required Skills:

Analytical Reasoning, Biochemistry, Biotechnology, Chemistry, Manufacturing, and Control (CMC), Clinical Research and Regulations, Clinical Trial Designs, Coaching, Critical Thinking, Drug Discovery Development, Emerging Technologies, Molecular Diagnostics, Organizing, Pharmacovigilance, Presentation Design, Process Improvements, Productivity Planning, Scientific Research

Preferred Skills:

Analytical Reasoning, Biochemistry, Biotechnology, Chemistry, Manufacturing, and Control (CMC), Clinical Research and Regulations, Clinical Trial Designs, Coaching, Critical Thinking, Drug Discovery Development, Emerging Technologies, Molecular Diagnostics, Organizing, Pharmacovigilance, Presentation Design, Process Improvements, Productivity Planning, Scientific Research