Principal Scientist, Biologics Optimization

Johnson & Johnson Innovative Medicine • Spring House, PA

10d • Onsite

About The Position

The Biologics Optimization team within Biologics Discovery is responsible for the design and optimization of lead biologic variants across multiple therapeutic modalities to achieve differentiated functional outcomes aligned with desired therapeutic mechanism of action (MoA). In this position, the Principal Scientist will serve as a scientific leader and subject matter expert in biologics optimization, driving and dissecting protein engineering strategies that translate molecular design into improved biological function across diverse therapeutic modalities. This role requires deep expertise in antibody and protein engineering, computational and AI/ML-enabled design approaches, and mechanism-driven biologics discovery expertise. The successful candidate will partner closely with Therapeutic Area scientists, computational scientists, protein engineers, and drug discovery teams to develop and implement optimization strategies that enhance potency, selectivity, efficacy, and overall therapeutic performance.
The Principal Scientist will be expected to establish and champion scientific best practices in function-driven optimization, leveraging detailed understanding of antibody structure-function relationships, protein sequence space, and biological pathways to guide engineering decisions. This role offers the opportunity to influence discovery programs across the portfolio while shaping future optimization capabilities and technologies within Biologics Discovery. This role offers an exciting opportunity to not only engage in the discovery and molecular engineering of biotherapeutics, but to develop new processes and technologies in collaboration with other functional group with the opportunity to lead project teams and interact with world-class Therapeutic Area scientists.

Requirements

• A minimum of a MS Degree in in Molecular Biology, Biochemistry, Biotechnology, Biomedical Engineering, structural biology or related field is required. PhD is highly preferred.
• A minimum of 3 years of relevant industry experience in biologics discovery, protein 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.
• Deep understanding of antibody structure-function relationships, including variable region architecture, framework and CDR design, sequence liabilities, affinity maturation, specificity engineering, and developability considerations.
• Strong understanding of probing therapeutic biology and demonstrated experience applying biological insights to address function-driven engineering challenges in wet-lab setting.
• Proven ability to evaluate therapeutic mechanisms of action and design optimization strategies that improve biological activity, efficacy, selectivity, or target engagement.
• 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.
• Experience leveraging AI/ML, computational biology, protein modeling, or data science approaches to support biologics optimization and engineering workflows.
• 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

• Candidates with a Master’s degree: 6 years of industry experience is preferred
• Candidates with a PhD: 3 years of industry experience is preferred
• Expertise in scientific computing (e.g. Python) and experience with in silico protein structure and sequence modeling using AI/ML methods to facilitate collaboration with computational and AI/ML functional teams
• Expertise in protein structure modeling and visualization tools such as Schrödinger, MOE, PyMOL, Chimera, or AlphaFold-based workflows.
• Experience with cell-based functional assays across oncology or immunology areas (e.g. T-cell redirection, immune modulation)
• Experience with display-based technologies for protein engineering and optimization, with preference for yeast display and/or mammalian display platforms.
• Experience/strong understanding of 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.
• Experience integrating large-scale experimental datasets with computational and AI/ML approaches to accelerate optimization decisions.
• Demonstrated ability to establish new scientific capabilities, influence strategy, and mentor scientists within a matrixed research environment.

Responsibilities

• Lead biologics optimization efforts across multiple therapeutic programs, translating target biology and desired mechanisms of action into rational protein engineering strategies.
• Serve as a subject matter expert in function-driven optimization: develop and champion novel development efforts aimed at fulfilling strategic long-term antibody design/optimization initiatives and validation.
• Develop optimization strategies that integrate antibody sequence, structure, developability, and biological function to achieve program objectives.
• Lead/participate on project teams to advance discovery portfolio projects. Act as a point of accountability for optimization engineering asks and manage project timelines independently.
• Partner with teams across TD/TA to define critical functional hypotheses and establish screening cascades that enable MoA-driven decision making.
• Apply computational, AI/ML, and data-driven approaches to guide variant design, library construction, sequence prioritization, and hit selection.
• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal