Principal Scientist, DSCS Digital Technologies–Laboratory Automation, Time Series Data Strategy (Hybrid) Full-time

Principal Scientist

We are seeking a Principal Scientist to join our DSCS Digital Technologies (DDT) organization, within Development Sciences and Clinical Supply (DSCS), located in either Rahway, NJ or West Point, PA.

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, data science, integrated instrumentation, and automated data enablement that seamlessly transition data from the physical labs to the digital world. We believe innovation is fueled by various teams that come together to build technologies that truly impact how our scientists conduct research. We're looking for a Principal Scientist to help make this vision real.

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 the manner in which process, product, and analytical understanding is gathered. The tools that we develop are as various as the team developing them, and in this Principal Scientist role, the chosen candidate will leverage digital technologies to enable chemistry, manufacturing, and controls (CMC) activities across our company's small molecule, biologics, and vaccines portfolio.

The chosen candidate will lead strategy, architecture, and delivery for time series data capabilities across the DSCS community, including integrating various time-series data streams into state-of-the-art, contextualized content that can be consumed by downstream data products, business data platforms, lineage solutions, AI-driven modeling workflows, dashboards, and scientific decision-making tools.

The chosen candidate will define and implement coherent approaches for time series data collection, processing, visualization, historization, contextualization, and movement across small molecules, biologics, and vaccines that span laboratory to commercial scales. This includes real-time data collected as a process is happening, near-time data processing and visualization, and post-batch monitoring and analysis. This role will help establish a comprehensive strategy with clear definitions, prioritization principles, and scalable platforms across real-time, near-time, and post-batch efforts that align short-term business solutions with sustainable long-term enterprise strategies. Areas of focus may include historian requirements and alternatives, persistent time series data, contextualization, ontology and unified namespace concepts, modern communication standards such as OPC and MQTT, GMP-aware design where needed, and flexible access patterns for scientific and operational consumers.

How You'll Make an Impact

• Design the lab of the future by developing technologies that integrate robotics, analytical instrumentation, and software into cohesive, high-performing solutions
• Deliver world-renowned science by partnering with researchers to understand experimental needs and convert them into scalable digital workflows
• Work at the intersection of multiple disciplines, collaborating with experts in automation, data science, modeling, IT, 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 digital technologies and automation platforms to enhance performance, usability, and reliability
• 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, 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, 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, Material Science or a closely-related field with at least 10 years of relevant experience

Required Experience and Skills

• Highly motivated, technology-centric scientist or engineer with a demonstrated track record of modernizing pharmaceutical development practices that include biologics, vaccines, and small molecule modalities.
• Demonstrated experience with time series data, continuous data streams, scientific data engineering, laboratory or pilot plant automation, process informatics, PAT, or related data-rich technology ecosystems.
• Ability to architect and communicate scalable approaches for real-time, near-time, and post-batch data collection, processing, visualization, historization, contextualization, and consumption.
• Experience translating scientific and operational needs into data requirements, technical requirements, user stories, or solution architectures across multiple stakeholder groups.
• Strong understanding of how data classifications, data dimensions, instrumentation, workflows, users, criticality, and lifecycle considerations influence solution design and prioritization.
• Ability to work across multiple IT product lines and enterprise capabilities while balancing near-term business needs with sustainable long-term support models.
• Excellent communication skills, demonstrated creativity, effective interpersonal skills, and ability to influence without direct authority in a matrixed environment.
• Ability to lead complex, cross-functional projects under compressed timelines in a dynamic environment.

Preferred Experience and Skills:

• Extensive experience with historians or time series platforms, including requirements definition, evaluation of alternatives, migration strategy, contextualization, or data access patterns.
• Familiarity with modern industrial and laboratory data communication standards and patterns, including OPC, unified namespace concepts, ontology structures, asset models, metadata models, and contextualized data products.
• Experience designing solutions for persistent time series data, GMP-aware systems where appropriate, and flexible access patterns for scientific, engineering, modeling, and operational consumers.
• Experience with laboratory and pilot plant data sources such as sensor data, reactor data, offline, on-line, and in-line analytical data, scalar/vector/array/cube data structures, and dimension-plus-time data representations.
• Background in data engineering, cloud or enterprise data platforms, data modeling, Python, R Studio, SQL, Databricks, visualization tools, API-based integration, or related technologies.
• Experience evaluating, developing, or deploying digital and data-rich methodologies that support process and product understanding, batch analysis and monitoring, and/or CMC decision-making.
• Demonstrated track record of leading teams in a cross-functional, collaborative manner that span automation, IT, data science, modeling, process, product, and analytical development, and manufacturing.
• Motivated to learn new skills, take on new challenges, and bring scientific curiosity to ambiguous, enterprise-scale data problems.

Required Skills:

Adaptability, Analytical Instrumentation, Automation, Biochemistry, Biopharmaceutical Industry, Business, Cross-Functional Teamwork, Data Access, Data Analysis, Data Engineering, Data Science, Digital Technology, Enterprise Data, Innovation, Innovative Thinking, Laboratory Automation, Leadership Mentoring, Pharmaceutical Development, Pharmaceutical Process Development, Pharmaceutical Sciences, Professional Collaboration, Project Prioritization, Quality by Design, Real Time Data

Preferred Skills:

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