Data & Lab Automation Scientist – Drug Product Development

GSK•Upper Providence, PA

About The Position

We are looking for a Data & Lab Automation Scientist to join our Drug Product Development team. This role bridges the lab bench and digital systems, focusing on optimizing experimental workflows through data and automation. The ideal candidate will understand our processes, identify automation opportunities, and partner with vendors to implement complex custom solutions.

Requirements

3+ years of hands-on experience in lab and data automation.
Lab automation proficiency: Experience with liquid handling platforms (Hamilton or Tecan) design, scope, and execute automated workflows.
Vendor management experience: Track record of working with external vendors on complex custom automation projects, from requirements definition through execution and troubleshooting.
Lab experience: Current or recent hands-on laboratory work in a pharmaceutical, biotech, or analytical chemistry environment.
Data and lab automation mindset: Comfortable with concepts around data automation, data capture architecture, and dashboarding; understanding of how to translate experimental workflows into structured data systems.

Nice To Haves

Experience with Autonomous Mobile Robots
Experience with data management practices in pharmaceutical drug product development
Proficiency in one or more programming/analytics languages or tools: Python, JavaScript, R, RShiny, SQL, Spotfire, Tableau
Experience designing or implementing data visualization dashboards to support decision-making
Familiarity with data extraction from laboratory instrumentation and integration into centralized data systems
Knowledge of digital lab solutions (ELN best practices, data standards, regulatory considerations in pharma data management)

Responsibilities

Drive lab automation strategy: Scope, design, and implement automated workflows to increase throughput and data quality in drug product studies.
Enable vendor partnerships: Scope and manage complex custom automation projects with external vendors, translating scientific needs into technical specifications and ensuring successful implementation.
Collaborate across teams: Work with drug development experts, analytics teams, and instrumentation specialists to define requirements for ELN templates, dashboards, and data capture systems that support automated workflows.
Streamline data flows: Contribute to automating data and information flows during experimental studies—from instrument data extraction to system integration, storage and visualization—reducing manual entry and improving data integrity.
Build organizational capability: Share expertise in lab and data automation best practices and help standardize approaches across groups.