Senior Manager Business Intelligence, AI & Data Governance, CVRM

Genentech•Holly Springs, NC

1d•Onsite

About The Position

The Senior Manager Business Intelligence, AI & Data Governance is the site's technical and strategic leader for data-driven operations. This working specialist role designs, builds, and maintains a comprehensive data ecosystem that delivers actionable intelligence across manufacturing, quality, and warehouse operations. The role builds dashboards, collaborates on data analysis, and establishes site data governance norms while leading and mentoring data engineers, BI developers, data scientists, and data governance specialists. It connects engineering and data architectures with day-to-day plant operations so that intelligence is actively used, continuously validated, and expanded to deliver measurable business impact. The position leads business intelligence strategy and technical delivery, artificial intelligence and predictive analytics initiatives, data governance and GxP compliance, and data architecture and integration. It also develops the team, tracks the value of BI, AI, and Digital Twin initiatives, and identifies additional manufacturing use cases and opportunities for improvement. The role is based in Holly Springs, North Carolina, at a new manufacturing facility planned to launch in 2028. Relocation assistance is available. The expected salary range is $133,400–$247,800, with a discretionary annual bonus potentially available based on individual and company performance.

Requirements

B.S. or M.S. in Chemical Engineering, Mechanical Engineering, Data Science, Computer Science, Industrial Automation, or a related technical field.
At least 8 years of experience in pharmaceutical manufacturing, process engineering, automation, data analytics, or industrial digital transformation.
Hands-on experience designing and building BI dashboards, analytics solutions, and data pipelines using SQL, Python/R, or equivalent.
Hands-on experience with simulation, Digital Twin solutions, or advanced data modeling, with demonstrated ability to drive insights and operational improvements.
Deep understanding of pharmaceutical plant operations, including GMP, batch process control, and PAT, and familiarity with data architectures and systems such as IoT, MES, SCADA, historian systems, and simulation software.
Proven track record managing product lifecycles, backlogs, and use-case prioritization, and translating complex business requirements into prioritized, actionable deliverables.
Strong background building business cases and demonstrating value realization through data-driven metrics.
Experience establishing and enforcing data governance policies, data lineage, and quality assurance mechanisms in GxP-regulated environments.
Demonstrated expertise in FDA 21 CFR Part 11 and ALCOA+ compliance.
Experience leading data governance initiatives outside ERP systems.
Demonstrated success in cross-functional stakeholder management, bridging technical teams and operations, engineering, and quality functions.
Ability to lead, mentor, and develop high-performing technical teams.
Experience driving organizational adoption of new technologies and methodologies.

Nice To Haves

Agile/Scrum Product Owner certification (CSPO/PSPO).
Lean Six Sigma Green or Black Belt.
Project Management Professional (PMP) certification.
GxP/ALCOA+ compliance certification or training.
Familiarity with simulation software such as AspenTech, Ansys, or Siemens.

Responsibilities

Design, deploy, and personally build site-wide BI dashboards, analytics pipelines, and KPI frameworks for operational, quality, and executive decision-making.
Analyze plant data to identify opportunities such as yield optimization, cycle-time reduction, energy efficiency, and quality improvements, and partner with business functions to turn insights into action.
Co-design data visualizations and analytics solutions with Operations, Quality, Engineering, and Warehouse teams.
Establish and manage analytics time-to-insight standards to reduce lead time for operational reports and batch disposition analytics.
Lead AI and machine-learning initiatives, including predictive maintenance, yield optimization, and real-time operator decision support.
Drive measurable improvements in batch yield, equipment uptime (OEE), or downtime reduction through predictive AI models.
Ensure AI model validation and governance align with GxP requirements.
Establish and enforce site-wide data governance policies, data lineage protocols, and data quality assurance mechanisms for non-ERP data sources, including SCADA, MES, LIMS, historian systems, and IoT platforms.
Ensure compliance with FDA 21 CFR Part 11 and ALCOA+ principles, and maintain adherence to GxP/ALCOA+ standards.
Partner with Quality Assurance and Compliance teams to align data integrity protocols and AI validation strategies.
Define site standards for data collection, storage, access, and usage.
Partner with IT/OT teams to design and oversee integration of data pipelines across SCADA, MES, LIMS, and ERP systems into a unified analytics data layer.
Collaborate on architectural decisions supporting scalability, security, and regulatory compliance.
Monitor data integration completeness and drive continuous improvement in system connectivity.
Build, mentor, and lead a high-performing team of data engineers, BI developers, data scientists, and data governance specialists.
Foster technical excellence, continuous learning, and cross-functional collaboration; provide hands-on technical mentoring and code reviews; and develop team members through coaching and growth opportunities.
Establish KPIs to quantify business value across BI, AI, and Digital Twin initiatives, and regularly report metrics and ROI to site leadership.
Identify manufacturing use cases, ROI opportunities, and continuous improvement projects to expand the Digital Twin's footprint and analytics capabilities.