Senior Manager Business Intelligence, AI & Data Governance, CVRM

Genentech•Cary, NC

2d•$133,400 - $247,800•Onsite

About The Position

The Senior Manager Business Intelligence, AI & Data Governance is the site's technical and strategic leader for data-driven operations. The role designs, builds, and maintains a data ecosystem that provides actionable intelligence across manufacturing, quality, and warehouse operations. It is a hands-on specialist position involving dashboard development, data analysis, and site-wide data governance, while also leading and mentoring data engineers, BI developers, data scientists, and data governance specialists. The role connects engineering and data architectures with day-to-day operations, ensuring intelligence is used, validated, and expanded to deliver measurable business impact. The position is based at Genentech's Holly Springs, North Carolina facility, a $2B greenfield manufacturing site planned to launch in 2028. The facility uses advanced automation, robotics, digital tools, real-time data analytics, and streamlined processes to manufacture CVRM medicines, with a portfolio focused on next-generation metabolic medicines for obesity and Type 2 diabetes. The role develops and delivers BI strategy and technical solutions; leads AI/ML initiatives and predictive analytics; establishes data governance, integrity, and GxP compliance standards; oversees data integration and architecture in partnership with IT/OT; leads and develops a technical team; and tracks business value and ROI across BI, AI, and Digital Twin initiatives. 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

Bachelor’s or master’s degree 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, paired with familiarity with data architectures and technologies such as IoT, MES, SCADA, historian systems, and simulation software.
Proven track record managing product lifecycles, backlogs, and use-case prioritization, translating complex business requirements into prioritized 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 certification.
Project Management Professional (PMP) certification.
GxP/ALCOA+ compliance certification or training.

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 operational opportunities, including yield optimization, cycle-time reduction, energy efficiency, and quality improvements, and partner with business functions to act on insights.
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 times for operational reports and batch disposition analytics.
Lead AI/ML initiatives, including predictive maintenance, yield optimization, and real-time operator decision support.
Drive measurable improvements in batch yield, equipment uptime (OEE), or downtime 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 data operations comply with FDA 21 CFR Part 11 and ALCOA+ principles, and maintain adherence to GxP/ALCOA+ standards.
Partner with Quality Assurance and Compliance teams on data integrity protocols and AI validation strategies.
Define site standards for data collection, storage, access, and use.
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 scalable, secure, and compliant architectural decisions; monitor data integration completeness and improve 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 mentoring and code reviews and develop team members’ technical and leadership capabilities.
Establish KPIs to quantify business value across BI, AI, and Digital Twin initiatives and report metrics and ROI to site leadership.
Identify new manufacturing use cases, ROI opportunities, and continuous improvement projects to expand the Digital Twin’s footprint and analytics capabilities.