Senior Manager Business Intelligence, AI & Data Governance, CVRM

Roche•Holly Springs, 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 comprehensive data ecosystem to deliver 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 position connects data and engineering architectures with daily operations, ensuring analytics are used, validated, and expanded to achieve measurable business impact. The role is based at Genentech’s new Holly Springs, North Carolina manufacturing facility, a $2 billion greenfield start-up designed for high-volume drug product manufacturing, with a planned launch in 2028. The facility uses advanced automation, robotics, digital tools, real-time data analytics, and streamlined processes to manufacture CVRM medicines, with capacity to grow alongside the portfolio. The position leads enterprise BI delivery, AI and predictive analytics, data governance and GxP compliance, data architecture and integration, team development, and value tracking. It establishes analytics and governance standards, supports regulatory compliance, and reports business value and ROI to site leadership. Relocation assistance is available.

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, together with familiarity with data architectures such as IoT, MES, SCADA, historian systems, and simulation software.
Experience establishing and enforcing data governance policies, data lineage, and quality assurance mechanisms in GxP-regulated environments.
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 communication among technical teams, operations, engineering, and quality functions.
Ability to lead, mentor, and develop high-performing technical teams.
Proven track record managing product lifecycles, backlogs, and use-case prioritization, and translating complex business requirements into actionable deliverables.
Strong background in building business cases and demonstrating value realization through data-driven metrics.
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 opportunities in yield, cycle time, energy efficiency, and quality, 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, reducing lead times 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 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 usage.
Partner with IT/OT teams to design and oversee integration of SCADA, MES, LIMS, and ERP data pipelines into a unified analytics data layer.
Collaborate on architecture decisions to ensure 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 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.
Manage product lifecycles, backlogs, and use-case prioritization; translate business requirements into prioritized deliverables and demonstrate value realization through data-driven metrics.
Drive organizational adoption of new technologies and methodologies.