Associate Principal Scientist – Laboratory Automation, Semantic Technologies, Scientific Data Integration

MSD

Associate Principal Scientist developing and deploying digital and data-rich technologies in pharmaceutical development. Collaborating with scientists to enhance laboratory automation and data integration capabilities.

Posted 7/9/2026full-timeRahway • New Jersey, Pennsylvania • 🇺🇸 United StatesJuniorMid-Level💰 $142,400 - $224,100 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing automation systems that integrate robotics, instrumentation, and software, while effectively collaborating with cross-functional teams to deliver impactful solutions in the life sciences domain. Proficient in ontology development, data integration, and scientific data management practices, with a strong focus on enhancing digital workflows.

Highest-signal resume keywords

Ph.D. In Chemistry, Biochemistry, Engineering, Physics, Biology, Pharmaceutical Sciences, Computer Science, Or Material ScienceOntology Development Or Management ExperienceProficiency In Ontology Languages And Tools, Such As OWL, RDF, ProtégéUnderstanding Of (Bio)Pharmaceutical Process Research And DevelopmentExperience Enabling AI-Ready Data Through Semantic Technologies

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Automation System DevelopmentData IntegrationScientific Data ManagementOntology DesignSemantic ModelingKnowledge RepresentationData EngineeringAnalytical Research And DevelopmentDigital Workflow IntegrationAI/ML Concepts Application

Soft Skills

Excellent Communication SkillsCreativityInterpersonal SkillsTeam CollaborationProblem-Solving

Tools & Technologies

OWLRDFProtégéSPARQLSKOSAllotropeCDISCGraph-Based Data ArchitecturesSemantic Web TechnologiesData/Metadata Standards

Industry Keywords

Life SciencesBiopharmaceuticalsDrug DevelopmentAnalytical DevelopmentSemantic Data ConceptsHeterogeneous Data SourcesDigital IntegrationKnowledge Graph InitiativesData-Rich TechnologiesScientific Data Integration

About the role

Key responsibilities & impact

Design the lab of the future by developing automation systems that integrate robotics, instrumentation, equipment, and software into cohesive, high-performing solutions
Translate science into systems by partnering with researchers to understand experimental needs and integrate them with scalable digital workflows
Work at the intersection of disciplines, collaborating with experts in automation, data science, modeling, IT, knowledge capture, and the CMC community at large
Lead high-impact projects from concept through deployment, working across teams and stakeholders to deliver meaningful outcomes
Continuously improve existing automation platforms to enhance performance, usability, and reliability
Connect the ecosystem by integrating instrument and equipment platforms with semantic context and data systems to enable end-to-end digital workflows
Empower others by providing hands-on partnership, support, troubleshooting, and training to scientists using these technologies

Requirements

What you’ll need

A Ph.D. in Chemistry, Biochemistry, Engineering (i.e., Mechanical, Electrical, Chemical), Physics, Biology, Pharmaceutical Sciences, Computer Science, Material Science or a closely related field with at least 3 years of relevant experience
A M.S. in Chemistry, Biochemistry, Engineering (i.e., Mechanical, Electrical, Chemical), Physics, Biology, Pharmaceutical Sciences, Computer Science, Material Science or a closely related field with at least 5 years of relevant experience
A B.S. in Chemistry, Biochemistry, Engineering (i.e., Mechanical, Electrical, Chemical), Physics, Biology, Pharmaceutical Sciences, Computer Science, Material Science or a closely related field with at least 7 years of relevant experience
Understanding of (bio)pharmaceutical process research and development, drug product development, and/or analytical development
Demonstrated ability to work in an entrepreneurial and independent manner on cross-functional teams
Understanding of ontologies, taxonomies, controlled vocabularies, metadata, and semantic data concepts
Working knowledge of ontology design, FAIR principles, data/metadata standards, and scientific data management practices
Understanding of semantic modeling, knowledge representation, and scientific data contextualization approaches
Experience working with heterogeneous scientific data sources and integrating information across multiple systems or domains
Highly motivated and technology-centric scientist passionate about modernizing development practices across biologics, vaccines, and small molecule modalities
Background and experience in data-rich technologies, data engineering, or scientific data integration
Demonstrated scientific ability through publications and presentations in scientific conferences
Excellent communication skills, demonstrated creativity, and effective 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
Proven experience in ontology development or management, preferably in the Life Science domain.
Proficiency in ontology languages and tools, such as OWL, RDF, Protégé, or similar.
Strong understanding of knowledge representation and reasoning techniques.
Ability to model concepts, entities, relationships, and rules in a machine-readable way
Familiarity with RDF, OWL, SPARQL, SKOS, or related semantic standards
Experience connecting heterogeneous data sources and resolving differences in terminology, structure, and quality
Familiarity with data integration and interoperability challenges in the Life Science sector.
Familiarity with ontological standards, semantic web technologies, and data modelling principles.
Experience developing or supporting knowledge graph initiatives and graph-based data architectures
Familiarity with life-science data standards, including Allotrope, CDISC, or comparable frameworks
Understanding of data science and AI/ML concepts and their application to data contextualization
Experience enabling AI-ready data through semantic technologies, metadata strategies, and scientific data integration
Background in leveraging a broad range of data engineering and data science technologies, including digital integration of analytical instrumentation
Experience in new technology research, including a demonstrated 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
Motivated to learn new skills, willingness to take on new challenges, and scientific curiosity

Benefits

Comp & perks

medical, dental, vision healthcare and other insurance benefits (for employee and family)
retirement benefits, including 401(k)
paid holidays
vacation
compassionate and sick days