Senior Scientist, DSCS Digital Technologies–Laboratory Automation, Scientific Data Integration (Hybrid)

MSD•Upper Gwynedd Township, PA

3d•$117,000 - $184,200•Hybrid

About The Position

The Senior Scientist will work within Development Sciences and Clinical Supply (DSCS) Digital Technologies to identify, develop, and deploy digital and data-rich technologies that improve process, product, and analytical understanding across small-molecule, biologics, and vaccine development. The role operates at the intersection of pharmaceutical science, digital technologies, and data integration to help create FAIR, AI-ready scientific data ecosystems that accelerate research and improve how scientific knowledge is generated, managed, and reused. The position advances automated, connected laboratories by applying industry-standard and emerging technologies to integrate, orchestrate, and visualize laboratory systems and workflows. Working collaboratively across laboratory automation, digital engineering, IT, and scientific teams, the Senior Scientist independently owns and delivers technical solutions that connect laboratory equipment, automation systems, and data pipelines into cohesive, scalable solutions.

Requirements

Ph.D. in Chemistry, Biochemistry, Engineering, Physics, Biology, Pharmaceutical Sciences, Computer Science, Data Science, Material Science, or a closely related field; expected graduation may be as late as December 2026; or M.S. in one of these fields with at least 2 years of relevant experience; or B.S. in one of these fields with at least 4 years of relevant experience.
Proficiency in Python and/or R and familiarity with development environments such as Jupyter, Posit/RStudio, or VS Code.
Experience with ETL/ELT processes and building data pipelines in a scientific or pharmaceutical context.
Familiarity with version control systems such as Git/GitHub and collaborative software development practices.
Understanding of (bio)pharmaceutical process research and development, drug product development, and/or analytical development.
Excellent communication, creativity, and interpersonal skills.
Ability to deliver complex solutions under compressed timelines in a dynamic environment.
Ability to work in a team environment with cross-functional interactions.
Ability to work entrepreneurially and independently on cross-functional teams.
Demonstrated scientific ability through publications and presentations at scientific conferences.

Nice To Haves

Understanding of knowledge representation and reasoning techniques.
Working knowledge of ontology design, FAIR principles, data and metadata standards, and scientific data management practices.
Understanding of semantic modeling, knowledge representation, and scientific data contextualization approaches.
Familiarity with life-science data standards, including Allotrope, CDISC, or comparable frameworks.
Experience working with heterogeneous scientific data sources and integrating information across multiple systems or domains.
Experience connecting heterogeneous data sources and resolving differences in terminology, structure, and quality.
Ability to model concepts, entities, relationships, and rules in a machine-readable way.
Familiarity with ontology frameworks or standardized data models such as Allotrope Simple Model, ISA-88, or OPC-UA, and experience mapping instrument data to structured schemas.
Understanding of data science and AI/ML concepts and their application to data contextualization.
Background leveraging data engineering and data science technologies, including digital integration of analytical instrumentation.
Experience researching new technologies and a track record of identifying, developing, and deploying digital and data-rich methodologies.
Wet-lab experience in (bio)pharmaceutical drug substance, drug product, and analytical research and development.
Motivation to learn new skills, take on new challenges, and demonstrate scientific curiosity.

Responsibilities

Integrate instrument and equipment platforms with semantic context and data systems to enable end-to-end digital workflows.
Collaborate with experts in automation, data science, modeling, IT, knowledge capture, and chemistry, manufacturing, and controls.
Partner with researchers to understand experimental needs and translate them into scalable digital workflows.
Lead high-impact projects from concept through deployment, coordinating across teams and stakeholders.
Develop laboratory automation systems that integrate robotics, instrumentation, equipment, and software.
Continuously improve existing automation platforms to enhance performance, usability, and reliability.
Provide hands-on partnership, support, troubleshooting, and training to scientists using the technologies.
Build and deliver technical solutions connecting laboratory equipment, automation systems, and data pipelines.