Associate Director, Data Science

Job Description About the Organization The Biologics Science and Technology Platforms, Data, Modeling, and Statistics (PDSM) organization is a highly technical, science-forward function embedded within Bio S&T. We partner closely with manufacturing sites, IT, and process engineers to deliver data-driven process insights, statistical modeling, and digital capabilities that accelerate biologics commercialization and manufacturing excellence. Our mission is to bridge the gap between traditional process engineering and modern data science. We build the foundational architectures, digital workflows, and analytical models that underpin every initiative across the biologics network—enabling proactive process monitoring (PPM), continued process verification (CPV), yield optimization, tech transfer, and AI-ready manufacturing. We work hand-in-hand with our IT and manufacturing partners to co-design process analytics platforms. Our team contributes deep bioprocessing domain understanding paired with technical data science capabilities, ensuring the right process parameters and quality attributes are captured, contextualized, and modeled to drive real operational outcomes. Position Summary The Associate Director, Advanced Process Analytics & Data Strategy is a technical individual contributor role within PDSM. The primary expectation is hands-on technical contribution applying data science, process modeling, and data architecture to optimize biologics manufacturing. The successful candidate will act as a trusted technical expert, leveraging their bioprocess engineering background to build analytics solutions, drive process standardization, and partner closely with scientists and digital teams. We are seeking candidates who fit the Domain-to-Data Professional profile: A bioprocess, biochemical, or regulated manufacturing engineer who has developed meaningful data science expertise through hands-on work with process, analytical, and batch data. You must be able to apply tools such as Python, R, SQL, and statistical modeling to support process characterization, digital analytics, root-cause investigations, and regulatory-ready manufacturing data products. Key Responsibilities 1. Biologics Process Analytics & Engineering Strategy Define and drive an advanced process analytics roadmap focused on connecting unit operations (upstream/downstream), process parameters (CPPs), and quality attributes (CQAs) through enterprise data models. Act as the vital bridge between bioprocessing science and data technology—translating complex manufacturing process dynamics into data requirements, and translating data capabilities into scientific and operational value (e.g., yield improvement, cycle time reduction). Lead process-focused data initiatives, assembling cross-functional teams (engineers, scientists, IT) to implement advanced analytics and digital capabilities across biologics manufacturing workflows. 2. Manufacturing Data Architecture & Contextualization Build and maintain a clear data flow map across the biologics manufacturing network, integrating core manufacturing systems (MES, LIMS, PI Historian, SAP, ELN). Lead process data contextualization and ontology mapping. Ensure raw process and analytical data is properly linked across unit operations, sites, and product lifecycle stages to enable seamless tech transfer and comparability studies. Partner with IT to co-design scalable, GxP-compliant data engineering solutions, providing the critical bioprocess domain context required to structure the data correctly for scientific use. Define and enforce data integrity specifications (ALCOA+) to ensure reliability and regulatory compliance across manufacturing data products. 3. Process Monitoring, Modeling & Statistical Enablement Develop and deploy fit-for-purpose dashboards, process visualizations, and analytics to enable Proactive Process Monitoring (PPM), trend identification, and rapid root-cause investigation support. Partner with the Statistical Sciences (CMS) and Process/Product Modeling teams to ensure the underlying data foundation robustly supports Continued Process Verification (CPV), digital twins, AI/ML models, and multivariate analysis. Collaborate with internal manufacturing sites and Contract Manufacturing Organizations (CMOs) to establish sustainable data access and improve the usability of process/analytical data for technical troubleshooting. 4. Process Governance & Standardization Establish process data standards, nomenclature, and data ownership models across the biologics network to enable cross-site comparability and AI readiness. Build data stewardship practices that are owned and sustained by the engineering and science teams, ensuring data governance is treated as a core manufacturing capability. 5. Stakeholder Engagement & Capability Building Drive alignment across Technical Product Managers, process SMEs, Quality, Regulatory Affairs, and IT to advance shared process-analytics priorities. Build digital and data literacy across Bio S&T by coaching peers, sharing engineering-focused use cases, and enabling governed self-service analytics through templates and training. Education Requirements B.S. in Chemical Engineering, Biochemical Engineering, Bioengineering, Life Sciences, or a related field with 8+ years of relevant biopharmaceutical experience. M.S. in the same fields with 6+ years of relevant experience, or Ph.D. with 4+ years of relevant experience. Required Experience and Skills Bioprocess Engineering & Domain Expertise Strong foundational knowledge of biologics manufacturing (Upstream/Downstream unit operations, scale-up, tech transfer) and process characterization. Proven experience utilizing process data (PI Historian, MES, LIMS) to troubleshoot manufacturing issues, monitor process performance, or support regulatory filings. Deep understanding of GMP/GxP environments, Continued Process Verification (CPV), and quality/compliance requirements in biomanufacturing. Data Science & Technical Engineering Hands-on experience with Python or R for data manipulation, statistical analysis, and scripting—applied specifically to scientific or manufacturing datasets. Moderate to strong hands-on SQL skills; ability to query, transform, and validate data across relational databases. Understanding of how to extract and structure time-series data (e.g., from PI/DeltaV) and relational batch data to build actionable process models. Familiarity with data architecture concepts (data lakes, data warehousing) and experience collaborating with IT/Data Engineering to operationalize analytical pipelines. Leadership, Strategy, and Communication Strategic thinking and independent execution capability — ability to define a data strategy roadmap and drive hands-on delivery against it without requiring a team to execute beneath you. Proven ability to influence without authority and drive alignment across technical, business, Digital, Quality, and external partner stakeholders. Strong change management skills — ability to drive adoption of new data practices and tools across a complex, globally distributed organization. Ability to translate complex technical and data concepts into clear, actionable recommendations for both technical and non-technical audiences. Comfortabl