Principal Scientist, DSCS Digital Technologies–Laboratory Automation, Semantic Technologies & Scientific Data Integration

Job description

Job Description Primary Responsibilities: In the Development Sciences and Clinical Supply (DSCS) organization, we believe digital is the force multiplier that enables faster experiments, smarter decisions, more robust supply chains, and is ultimately redefining how we bring medicines to patients. Within DSCS, the DSCS Digital Technologies (DDT) team is building the next generation of data-rich, connected lab environments— where hardware, software, and AI-driven tools work together to accelerate pharmaceutical development. Our goal is simple but ambitious: embed advanced digital capabilities into the DNA of how we work. We believe this future starts with a strong foundation of laboratory tools: innovative robotics, process analytical technology (PAT), high-throughput technologies, reliable instrumentation, and integrated systems that seamlessly contextualize data and transition it from the physical labs to the digital world. Our innovation is fueled by diverse teams coming together to build technologies that truly impact how our scientists conduct research. We’re looking for a Principal Scientist to help realize this vision. In this role, the chosen candidate will work with a team of scientists tasked with identifying, developing, and deploying digital and data-rich technologies aimed at improving Process, Product, and Analytical understanding. The tools that we develop are as diverse as the team developing them, and in this role, the chosen candidate will leverage digital technologies to enable chemistry, manufacturing, and controls (CMC) activities across our small molecule, biologics, and vaccines portfolio. This role will focus on developing and applying ontology-driven and semantic technologies that transform complex scientific information into structured, contextualized, and reusable knowledge. This includes leveraging knowledge representation, semantic enrichment, metadata strategies, and scientific data integration approaches to connect laboratory systems, instruments, data platforms, and scientific workflows. The successful candidate will help establish the semantic foundation that supports next-generation digital laboratories, AI-enabled scientific workflows, and connected pharmaceutical development. Working at the intersection of pharmaceutical science, digital technologies, and data integration, the candidate will support the development of semantic models, ontologies, and knowledge graph frameworks that improve interoperability, traceability, discoverability, and consistency across scientific systems. Through these efforts, the candidate will help enable FAIR, AI-ready scientific data ecosystems that accelerate research, enhance decision making, and improve the way scientific knowledge is generated, managed, and reused across the development lifecycle. One of the most unique aspects of this role is the opportunity to work directly with scientists developing biologics, vaccines, and small molecules while helping build the semantic and digital foundations that power next-generation pharmaceutical development. Rather than operating as a traditional informatics or IT function, this role is deeply embedded within scientific research and development, ensuring that emerging digital and semantic capabilities create meaningful impact for laboratory scientists and development teams. How You’ll Make an 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 Education Minimum Requirement: 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 6 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 8 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 10 years of relevant experience Required Experience and Skills: 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 Familiarity with ontological standards, semantic web technologies, and data modelling principles. Experience enabling AI-ready data through semantic technologies, metadata strategies, and scientific data integration 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 Preferred Experience and Skills: 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. 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 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 an