Director, Protein Engineering, Discovery Portfolio
$246.97k - $299.27kBristol Myers Squibb
At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it. Position Summary We are seeking a Director of Protein Engineering to lead the design and optimization of therapeutic proteins in support of the discovery portfolio across Oncology, Immunology, Cardiovascular, and Neuroscience. Working in close partnership with therapeutic area strategy and discovery leadership, the Director sets protein engineering strategy for the portfolio and is accountable for executing it program by program, translating therapeutic area priorities into format and molecule design strategies matched to the biology, the patient population, and the intended route and duration of therapy. The Director is a visible scientific leader on discovery project teams, shaping program strategy and driving molecules from concept to development candidate nomination. This role will lead a team of protein engineers and scientists and set the engineering strategy for biologics and antibody-derived molecules, conjugates, and fusion proteins. The Director also serves as the protein engineering interface to the Protein Sciences and Development organization, ensuring that format strategies chosen in discovery are aligned both with therapeutic area strategy and with what can be expressed, purified, characterized, and manufactured at scale. The Director works closely with computational and machine learning colleagues to execute on a closed-loop discovery strategy, coupling generative and molecule design, structure prediction, and sequence-based models to high throughput experimentation so that each cycle produces both better molecules and data that improve the next design. Key Responsibilities
- Lead the protein engineering team; set scientific direction, hire and develop scientists, and deploy capability against portfolio priorities.
- Own engineering strategy on discovery programs — format selection, affinity and specificity targets, epitope strategy, valency and geometry, humanization, and liability remediation — from concept through lead optimization to development candidate nomination.
- Co-develop the computational–experimental design strategy with machine learning and computational leaders, deciding jointly where generative design and model-guided prioritization are deployed across the portfolio.
- Partner with Oncology, Immunology, Cardiovascular, and Neuroscience strategy and discovery leadership to align protein engineering strategy with therapeutic area priorities: where a biologic can address the biology, which formats and modalities fit the target product profile, and what it will take to deliver them. Convert those decisions into engineering plans, resourcing, and program delivery.
- Serve as the protein engineering interface to the Protein Sciences and Development organization, ensuring that format selection and engineering choices are developable and manufacturable across expression and titer, purification, chain pairing and product quality, stability and formulation, so that molecules transition into development without late-stage rework.
- Partner with Discovery Biology, Structural Biology, DMPK, Toxicology, Translational Sciences, and Bioconjugation so that engineering choices made in discovery remain viable downstream.
- Champion linked sequence–structure–assay data with traceable metadata and quality standards in shared ownership with machine learning, informatics, and automation partners; represent protein engineering at governance and portfolio reviews; evaluate external technologies, manage academic, technology-partner, and CRO collaborations, and contribute to the patent estate.
- Bachelor's Degree 15+ years of academic / industry experience OR Master's Degree 12+ years of academic / industry experience OR PhD 8+ years of academic / industry experience.
- 6+ years of leadership experience.
- Degree in protein engineering, biochemistry, molecular biology, structural biology, bioengineering, immunology, or a related scientific discipline.
- Deep, demonstrated expertise in therapeutic protein engineering: structure–function reasoning, affinity and specificity optimization, epitope-directed design, humanization, and rational library design or directed evolution, applied to antibody and antibody-derived formats.
- Experience with multispecific design — geometry, valency, chain pairing, and the developability consequences of format choices — or with conditional, masked, or pH-dependent activity.
- Demonstrated program impact: molecules engineered or campaigns led that progressed through lead optimization to development candidate nomination.
- Proven project leadership: leading discovery project teams or serving as the biologics lead on them, with the strategic judgment to set program direction and a track record of delivering development candidates.
- Demonstrated people leadership: hiring, developing, and retaining scientists, and building complementary expertise across a team.
- Excellent written and verbal communication; proven ability to influence senior stakeholders and drive alignment across functions.
- 8+ years of progressive leadership experience, including accountability for multiple concurrent discovery programs and a track record of repeat candidate nominations.
- Fc engineering experience: effector function modulation, FcRn, and half-life extension.
- Experience delivering format-level solutions such as Fc effector function and half-life, multispecific architecture, conditional activity, and tissue-directed delivery, including receptor-mediated transcytosis and brain exposure strategies.
- Experience with high-throughput expression and purification workflows, including small-scale transient expression and quantitative binding and functional characterization, to rapidly screen formats and binders.
- Structure-based modeling: working knowledge of physics-based structural suites (Rosetta, FoldX, MOE, Schrödinger, or PyMOL) for energy minimization, mutational impact prediction, structural alignment, and rigidbody docking.
- Generative AI and ML-driven design: working knowledge of modern frameworks (AlphaFold 3, RFdiffusion, ProteinMPNN, or ESMFold) for sequence selection, de novo scaffold and linker design, and in silico screening, with the judgment to distinguish model confidence from measured function.
- Experience partnering on active learning, Bayesian optimization, or uncertainty-aware candidate selection in a real program, including the experimental design decisions that make such campaigns work.
- Experience with antibody engineering for conjugate modalities, including site-specific conjugation interfaces and the antibody-side properties that determine conjugate performance.
- Strong external scientific reputation: publication record, patents, invited presentations.
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