Senior Scientist, High-Throughput Biologics Production and Discovery Platforms Full-time

Senior Scientist, High-Throughput Biologics Production and Discovery Platforms

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 an experienced and highly motivated Senior Scientist to lead and expand high-throughput biologics production capabilities within Discovery Biotherapeutics. This hands-on, laboratory-facing leadership role will oversee the design, execution, optimization, and continuous advancement of integrated protein expression, purification, and quality-control workflows supporting antibody, multispecific, and other biologics discovery programs.

The successful candidate will bring deep expertise in transient mammalian protein production, laboratory automation, workflow development, and operational management. The candidate will have a demonstrated record of delivering large, plate-based production campaigns across multiple programs in a fast-paced discovery environment while maintaining high standards for quality, data integrity, safety, and on-time delivery.

This individual will serve as both a technical authority and an operational leader, building and sustaining a scalable protein production platform that generates decision-ready data and reliably delivers critical reagents and therapeutic molecules. The role requires close partnership with Protein Sciences, Molecular Biology, Automation, Data Science, Computational Biology, Discovery Biology, and project teams to connect experimental execution with portfolio priorities and integrated discovery workflows.

The ideal candidate combines an exceptional wet-lab foundation with enthusiasm for working alongside automation, data science, and computational colleagues. Experience with workflow orchestration or machine-learning-guided discovery is valued but is not required; candidates who have built strong experimental platforms and want to connect them with computational design are encouraged to apply.

Key Responsibilities

• Lead the build-out, scaling, and day-to-day operation of high-throughput protein production workflows supporting antibody, multispecific, and other biologics discovery programs.
• Develop and sustain integrated expression, purification, sample-normalization, and quality-control workflows capable of reliably supporting 60 or more plates per week across multiple concurrent programs.
• Provide technical leadership for transient mammalian and cell-free expression platforms, including HEK293 and CHO systems, from DNA preparation through purified protein delivery.
• Lead the operation, maintenance, troubleshooting, qualification, and strategic expansion of liquid handlers, robotic workstations, automated purification systems, and integrated analytical instrumentation.
• Establish production planning, capacity management, prioritization, performance metrics, and risk-mitigation practices to ensure predictable and timely delivery of high-quality molecules.
• Drive workflow development, technology evaluation, process miniaturization, and automation implementation to improve throughput, reproducibility, quality, and operational efficiency.
• Partner with Protein Sciences, Molecular Biology, Automation, Data Science, Computational Biology, Discovery Biology, and project teams to align production plans with portfolio priorities and experimental timelines.
• Collaborate with automation, data, and computational teams to advance closed-loop discovery workflows and enable structured, traceable data flow from design through experimentation and analysis.
• Establish and maintain best practices, standard operating procedures, quality standards, data-integrity expectations, and fit-for-purpose operational documentation.
• Lead root-cause analysis and resolution of complex production, workflow, and instrumentation issues, and implement corrective, preventive, and continuous-improvement measures.
• Analyze scientific and operational data to identify bottlenecks, reduce cycle time, improve success rates, and expand platform capacity.
• Mentor and train scientists and research associates, fostering a culture of technical excellence, accountability, collaboration, innovation, and safe laboratory practices.
• Represent the high-throughput production function on cross-functional teams and contribute to platform strategy, resource planning, and capability-development decisions.

Qualifications & Experience

• Bachelor's degree in bioengineering, structural biology, biochemistry, molecular biology, or a related scientific discipline with 7 or more years of relevant academic and/or industry experience; or a master's degree with 5 or more years of relevant experience; or a Ph.D. with 2 or more years of relevant experience.
• Demonstrated success leading large-scale, plate-based protein production operations with high sample volumes, multiple concurrent programs, and demanding delivery timelines.
• Extensive hands-on experience designing, operating, managing, and supporting high-throughput protein expression and production workflows in mammalian systems.
• Deep expertise in transient mammalian expression platforms, including HEK293 and CHO systems.
• Strong knowledge of protein purification technologies, including affinity chromatography, automated purification, sample normalization, and high-throughput quality assessment.
• Demonstrated expertise in molecular biology, including the design, optimization, and implementation of scalable cloning strategies and high-throughput DNA fragment generation workflows.
• Significant experience operating, maintaining, troubleshooting, and optimizing laboratory automation platforms, liquid handlers, robotic workstations, and integrated instrumentation.
• Experience with analytical methods used for protein quality assessment, including SEC, HPLC, SDS-PAGE, Octet, or related technologies.
• Demonstrated experience developing scalable workflows, implementing new technologies, and using operational metrics to improve throughput, reliability, quality, and cycle time.
• Proven ability to lead complex technical initiatives, independently resolve workflow challenges, manage competing priorities, and deliver results in a matrixed environment.
• Strong scientific leadership, mentoring, project-management, organizational, collaboration, and communication skills.

Preferred Qualifications

• Experience with laboratory automation programming and workflow scripting using platforms such as Hamilton VENUS, Tecan FluentControl, Agilent VWorks/Bravo, or comparable systems.
• Experience with instrument integration, device-driver configuration, interfaces, or automated data-transfer workflows.
• Familiarity with laboratory orchestration or scheduling software such as Green Button Go, Cellario, Momentum, Overlord, or similar platforms, including integration of standalone instruments into scheduled workcells.
• Working proficiency in Python or R for data handling, workflow scripting, and analysis of production and quality-control datasets.
• Experience with ELN, LIMS, or registration systems (e.g., Benchling), including programmatic or API-enabled data capture and preparation of experimental data for downstream computational use.
• Experience in closed-loop, lab-in-the-loop, or model-in-the-loop discovery environments integrating laboratory automation, data analytics, and AI-guided design or decision-making.
• Familiarity with machine-learning applications in protein engineering and how experimental data are used by protein structure prediction and design tools, protein language models, active-learning approaches, or multi-parameter optimization. Depth in machine-learning methods is not expected; effective collaboration with data science and computational colleagues is essential.