Sr Scientist, Process Development
Gilead Sciences
6 months ago
On-site
San Francisco Bay Area
Operations
Senior Scientist – Purification Automation, High-Throughput Robotics & Modeling
Key Responsibilities
- Purification Automation & High-Throughput Experimentation
- Design, implement, and continuously improve automated, high-throughput purification assays, including miniaturized resin screening and binding studies
- Develop and maintain scalable, reproducible robotic workflows using liquid handling and automation platforms
- Translate traditional column scale purification concepts into low-mass, high-information screening formats that support rapid decision making
- Mechanistic Modeling & Data-Driven Insight
- Develop and apply mechanistic models (e.g., adsorption, binding kinetics, transport phenomena) to interpret purification data and guide experimental design and operating window definition
- Apply multivariate analysis, machine learning, and statistical modeling to analyze complex datasets, identify trends, and predict purification performance under varying conditions
- Integrate experimental and computational approaches to improve understanding of resin–protein interactions, process sensitivities, and scale-relevant behavior
- Process Development Impact
- Enable early identification of process risks, sensitivities, and operating windows to support purification process design
- Inform resin selection, buffer strategies, and process parameters using data-driven insights
- Contribute to platform and capability development that benefits multiple programs across the Biologics portfolio
- Collaboration & Communication
- Partner closely with cross-functional teams in Purification, Automation, Analytics, and Data Science
- Communicate results through technical reports, presentations, and data visualizations tailored to diverse audiences
- Contribute to internal best practices, knowledge sharing, and long-term technology strategy within Purification Development
Qualifications
- Ph.D. with 2+ years (Senior Scientist)
OR
- B.S./M.S. with substantial industry experience in a relevant scientific discipline (chemical engineering, bioengineering, chemistry, or biochemistry)
- Strong foundation in protein purification, chromatography mechanisms, and downstream process development
- Hands-on experience with laboratory automation and high-throughput robotics (e.g., Hamilton, Tecan, or similar)
- Experience designing and executing miniaturized purification, binding, or screening assays
- Background in mechanistic modeling of purification or related physical/chemical processes
- Demonstrated ability to independently design, analyze, and troubleshoot complex experimental systems
- Strong problem-solving skills connecting experimental observations to mechanistic and quantitative understanding
- Excellent written and verbal communication skills; collaborative, team-oriented mindset
Preferred Qualifications
- Experience applying machine learning, multivariate analysis, or advanced statistics to experimental datasets
- Proficiency in Python, R, MATLAB, or similar tools for data analysis, modeling, and automation integration
Compensation/Benefits (if eligible)
- Salary range: $169,320.00 - $219,120.00
- May be eligible for a discretionary annual bonus, discretionary stock-based long-term incentives, paid time off, and a benefits package including company-sponsored medical, dental, vision, and life insurance plans.
Application Instructions
- For current Gilead employees and contractors: Apply via the Internal Career Opportunities portal in Workday.
Key Responsibilities
- Purification Automation & High-Throughput Experimentation
- Design, implement, and continuously improve automated, high-throughput purification assays, including miniaturized resin screening and binding studies
- Develop and maintain scalable, reproducible robotic workflows using liquid handling and automation platforms
- Translate traditional column scale purification concepts into low-mass, high-information screening formats that support rapid decision making
- Mechanistic Modeling & Data-Driven Insight
- Develop and apply mechanistic models (e.g., adsorption, binding kinetics, transport phenomena) to interpret purification data and guide experimental design and operating window definition
- Apply multivariate analysis, machine learning, and statistical modeling to analyze complex datasets, identify trends, and predict purification performance under varying conditions
- Integrate experimental and computational approaches to improve understanding of resin–protein interactions, process sensitivities, and scale-relevant behavior
- Process Development Impact
- Enable early identification of process risks, sensitivities, and operating windows to support purification process design
- Inform resin selection, buffer strategies, and process parameters using data-driven insights
- Contribute to platform and capability development that benefits multiple programs across the Biologics portfolio
- Collaboration & Communication
- Partner closely with cross-functional teams in Purification, Automation, Analytics, and Data Science
- Communicate results through technical reports, presentations, and data visualizations tailored to diverse audiences
- Contribute to internal best practices, knowledge sharing, and long-term technology strategy within Purification Development
Qualifications
- Ph.D. with 2+ years (Senior Scientist)
OR
- B.S./M.S. with substantial industry experience in a relevant scientific discipline (chemical engineering, bioengineering, chemistry, or biochemistry)
- Strong foundation in protein purification, chromatography mechanisms, and downstream process development
- Hands-on experience with laboratory automation and high-throughput robotics (e.g., Hamilton, Tecan, or similar)
- Experience designing and executing miniaturized purification, binding, or screening assays
- Background in mechanistic modeling of purification or related physical/chemical processes
- Demonstrated ability to independently design, analyze, and troubleshoot complex experimental systems
- Strong problem-solving skills connecting experimental observations to mechanistic and quantitative understanding
- Excellent written and verbal communication skills; collaborative, team-oriented mindset
Preferred Qualifications
- Experience applying machine learning, multivariate analysis, or advanced statistics to experimental datasets
- Proficiency in Python, R, MATLAB, or similar tools for data analysis, modeling, and automation integration
Compensation/Benefits (if eligible)
- Salary range: $169,320.00 - $219,120.00
- May be eligible for a discretionary annual bonus, discretionary stock-based long-term incentives, paid time off, and a benefits package including company-sponsored medical, dental, vision, and life insurance plans.
Application Instructions
- For current Gilead employees and contractors: Apply via the Internal Career Opportunities portal in Workday.