Principal Scientist, Biologics Optimization

Johnson & Johnson Innovative Medicine•Spring House, PA

2d•Hybrid

About The Position

The Biologics Optimization team within Biologics Discovery is responsible for accelerating biologic drug discovery by integrating experimental data, engineering insights, and emerging technologies to enable differentiated therapeutic outcomes. In this position, the Principal Scientist, Optimization Platform Integration, will serve as a scientific leader responsible for transforming discovery data into actionable knowledge and scalable capabilities that advance molecule optimization and platform development. Leveraging expertise at the intersection of biologics discovery, antibody engineering, data-driven research, and laboratory automation, this individual will uncover relationships between sequence, structure, function, and biological outcomes to establish design principles that guide optimization strategies. Through deep interrogation and integration of diverse data sources and development of automation-enabled workflows, the successful candidate will create frameworks that not only generate scientific insight but also systematically apply those learnings across optimization platforms. Working closely with Therapeutic Area scientists, protein engineers, computational scientists, and automation teams, this leader will drive the evolution of next-generation yeast display, NGS, and high-throughput optimization capabilities, ensuring that data generation, analysis, and experimental execution are tightly connected. The Principal Scientist will champion a data-driven culture within Biologics Optimization by developing approaches to organize, leverage, and operationalize experimental data at scale, while providing technical leadership for automation integration efforts that enhance reproducibility, throughput, and decision quality. By combining scientific insight, automation innovation, and platform development, this individual will help establish a continuous learning environment in which data generated from discovery efforts directly informs future engineering strategies, accelerates timelines, and improves portfolio-wide optimization outcomes. While based in Spring House, PA, the role will include periodic travel to the Cambridge, MA site to ensure alignment on technology development, infrastructure priorities, and implementation of automation capabilities.

Requirements

A minimum of a MS Degree in in Molecular Biology, Biochemistry, Biotechnology, Biomedical Engineering, structural biology or related field is required.
A minimum of 3 years of relevant industry experience in biologics discovery, protein engineering, computational biology, automation engineering or therapeutic optimization is required.
Demonstrated scientific leadership and expertise in biologics optimization with a strong focus on improving functional outcomes aligned with therapeutic mechanisms of action.
Extensive experience in antibody and protein engineering with a demonstrated ability to advance therapeutic molecules through optimization cycles.
Demonstrated experience leading automation builds that support discovery workflows, including collaboration with automation integrators.
Experience integrating large-scale experimental datasets with computational and AI/ML approaches to accelerate optimization decisions.
Proven ability to evaluate therapeutic mechanisms of action and design optimization strategies that improve biological activity, efficacy, selectivity, or target engagement.
Deep understanding of antibody structure-function relationships, including variable region architecture, framework and CDR design, sequence liabilities, affinity maturation, specificity engineering, and developability considerations.
Proficiency in scientific computing, such as Python, and experience using AI/ML-enabled protein sequence and structure modeling tools, including Schrödinger, MOE, PyMOL, Chimera, or AlphaFold-based workflows, to support effective collaboration with computational and AI/ML teams.
Zeal for addressing complex scientific questions and solving novel challenges with creativity and a collaborative spirit. Demonstration of successful implementation of new ideas with calculated risk taking. Abilities to lead by influence and communicate across teams with diverse backgrounds is required.
Demonstration of clear, concise, and timely communication skills, exemplified by peer-reviewed publications is required.

Nice To Haves

PhD is highly preferred.
Candidates with a Master’s degree: 6 years of industry experience is preferred
Candidates with a PhD: 3 years of industry experience is preferred
Strong understanding of biophysical and biochemical methods used for antibody and protein characterization, including techniques such as SPR, ELISA, flow cytometry, thermal stability analysis, aggregation assessment, and other developability metrics.
Hands-on experience with cell-based functional assays across oncology or immunology areas (e.g. T-cell redirection, immune modulation)
Hands-on experience with high-throughput binding assays and screening technologies, including ELISA, SPR, MSD, flow cytometry, or related platforms.
Strong understanding of mammalian protein expression systems, including transient expression and recombinant protein production workflows.
Strong understanding of NGS technologies; hands-on experience with techniques (e.g. Illumina, PacBio)
Demonstrated ability to establish new scientific capabilities, influence strategy, and mentor scientists within a matrixed research environment.

Responsibilities

Provide scientific leadership for automation-enabled discovery workflows, helping define future capabilities that increase throughput, reproducibility, and data generation across biologics optimization platforms.
Partner closely with automation teams across sites to support implementation, continuous improvement, and long-term evolution in automation of yeast display, NGS and related discovery technologies.
Partner with Therapeutic Area, Discovery, and Technology teams to identify key scientific questions and leverage data-driven analyses to guide decision-making across the biologics portfolio.
Collaborate with computational scientists, AI/ML experts, and experimental teams to apply advanced analytical approaches that enhance understanding of biological drivers and improve discovery efficiency.
Serve as a subject matter expert in scientific intelligence and knowledge generation, developing approaches to transform experimental data into actionable understanding of sequence-function, structure-function, and mechanism-function relationships.
Evaluate emerging technologies, analytical methods, and automation solutions to identify opportunities for accelerating biologics discovery and expanding organizational capabilities.
Lead and contribute to cross-functional initiatives that strengthen the connection between experimental execution, data generation, scientific interpretation, and portfolio strategy.
Evaluate emerging technologies and develop innovative approaches to accelerate biologics optimization workflows and therapeutic discovery.
Independently present/defend scientific findings in multi-functional project teams/initiatives.
Independently plan, analyze and interpret experimental results and draft patent applications, manuscripts, protocols, SOPs, technical reports, etc.