Senior Specialist, Manufacturing Data Engineering & Digital Integration

Job Description We aspire to be the premier research-intensive biopharmaceutical company. At the forefront of research, we deliver innovative health solutions that advance the prevention and treatment of diseases in people and animals. Join our team and use the power of leading-edge science to save and improve lives around the world. The Senior Specialist, Manufacturing Data Engineering & Digital Integration serves as a senior technical leader for the design, integration, delivery, and sustainment of manufacturing data platforms and connected digital solutions across GMP operations. The role transforms complex manufacturing, laboratory, automation, and enterprise data into scalable data products, analytics, and AI-enabled capabilities that improve operational performance, process robustness, and data-driven decision making. Responsibilities Provide senior technical leadership for manufacturing data engineering, analytics, and digital integration initiatives of varied scope and duration. Translate complex business and manufacturing challenges into scalable technical strategies, solution architectures, and executable delivery plans. Lead and coordinate cross-functional technical work, mentor data engineers and analysts, review deliverables, and promote scientific rigor, innovation, and continuous improvement. Design and direct integrations among manufacturing, laboratory, automation, and enterprise systems, including MES, process historians, SCADA/DCS, LIMS/CDS, ERP, and cloud analytics environments. Develop and oversee reliable data pipelines, APIs, web services, and integration patterns that move and contextualize information across OT and IT platforms. Define data requirements, interface specifications, data models, and architecture decisions for complex digital and capital projects. Lead troubleshooting and resolution of high-impact data flow, connectivity, interface, and performance issues affecting business operations. Own the end-to-end lifecycle of manufacturing data products from discovery and requirements through prototyping, industrialization, deployment, adoption, and ongoing sustainment. Direct the configuration and administration of manufacturing data and analytics platforms such as AVEVA PI System, SEEQ, Power BI, data lakes, and cloud-based analytics environments. Prepare, structure, model, and contextualize varied manufacturing datasets for dashboards, self-service analytics, advanced analytics, and operational reporting. Design scalable and reproducible analytical workflows that integrate process automation, laboratory, quality, and environmental monitoring data. Lead the evaluation, design, and deployment of AI and machine learning solutions for manufacturing use cases, including predictive analytics, process monitoring, deviation reduction, operational efficiency, knowledge management, and personal productivity. Guide development and integration of AI-powered applications, digital assistants, generative AI capabilities, and, where appropriate, computer vision solutions using approved enterprise platforms and APIs. Assess technical feasibility, data readiness, model risk, business impact, scalability, and sustainment requirements before industrializing AI solutions. Ensure data pipelines, models, dashboards, integrations, and applications follow internal data governance, data integrity, cybersecurity, and GxP/cGMP requirements. Provide leadership throughout the System Development Life Cycle, including requirements, design, configuration or development, testing, validation, deployment, release management, change control, and ongoing support. Lead or support validation and qualification activities for regulated computerized systems and maintain documentation needed for inspection and audit readiness. Establish data quality monitoring, metadata, lineage, access, and governance practices that promote trusted and reusable manufacturing data. Lead technical delivery for digital, automation, and capital projects requiring manufacturing system integration, data engineering, analytics, or AI capabilities. Partner effectively with Manufacturing, Engineering, Quality, Technical Operations, Laboratories, IT/OT, external vendors, and global digital organizations. Communicate complex technical, analytical, and statistical concepts clearly to technical contributors, business stakeholders, and senior leaders. Qualifications Required Bachelor's degree in data science, Computer Science, Software Engineering, Data Engineering, Information Systems, Computer Engineering, Engineering, Mathematics, Statistics, or a related quantitative or technical discipline with minimum of five (5) years of applied experience in data engineering, system integration, manufacturing systems, data analytics, data science, or AI/ML solution development. Demonstrated experience leading, mentoring, or directing technical contributors or complex cross-functional projects. Advanced proficiency in Python and SQL, with working knowledge of additional languages or scripting tools such as R, PowerShell, or JavaScript. Hands-on experience with data integration technologies, REST APIs, web services, relational databases, data modeling, and enterprise integration patterns. Experience working within regulated environments and applying SDLC, computerized system validation, data integrity, and cybersecurity practices. Preferred Master's degree in Data Science, Computer Science, Engineering, Mathematics, Statistics, or a related field with three (3) years of applied experience in data engineering, system integration, manufacturing systems, data analytics, data science, or AI/ML solution development. Strong knowledge of biopharmaceutical manufacturing operations, continuous process improvement, and cGMP regulatory environments. Experience with AVEVA PI System, SEEQ, Power BI, Ignition, Spotfire, Tableau, Dataiku, or comparable historian, analytics, and self-service BI platforms. Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn and cloud data platforms such as Azure or AWS. Experience with advanced manufacturing analytics, digital twins, large language models, generative AI, or computer vision, with emphasis on measurable business impact. Ability to explain complex engineering, data, AI, and statistical concepts to non-technical business leaders and influence decisions #J-18808-Ljbffr HireLifeScience.com