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About This Role
The Position
Welcome to Holly Springs, North Carolina— home to Roche/Genentech's largest-ever investment: a $2B greenfield start-up where next-generation technology, high-performing teams and Operational Excellence converge to create the global standard for high-volume, highly-efficient Drug Product manufacturing.
The facility's core is defined by Innovation - leveraging advanced automation, robotics, digital tools, real-time data analytics, and simple, streamlined processes to produce the highest quality medicines. This plant meets today’s demand for CVRM (Cardiovascular, Renal, and Metabolism) medicines and is scalable to grow with the portfolio, which focuses on next-generation metabolic medicines specifically targeting the global obesity epidemic and Type 2 diabetes. This is a fast-paced, entrepreneurial environment with a high-level of accountability, with planned launch in 2028.
Are you a problem-solver and change-agent who thrives in a Team environment, eager to tackle the most complex manufacturing challenges? If so, this is a once-in-a-lifetime opportunity to launch a state-of-the-art facility from the ground-up and shape the culture of our organization for the next generation.
Join us in Holly Springs, where you won't just build a plant—you will build the future of medicine.
The Opportunity
The Senior Manager Business Intelligence, AI & Data Governance is the site's technical and strategic leader for data-driven operations, responsible for designing, building, and maintaining a comprehensive data ecosystem that drives actionable intelligence across manufacturing, quality, and warehouse operations. This is a working specialist role—you will design and build dashboards, collaborate on data analysis, and establish data governance norms across the site, while simultaneously leading and mentoring a high-performing team of data engineers, BI developers, data scientists, and data governance specialists. You bridge complex engineering and data architectures with daily manufacturing, quality, and warehouse operations, ensuring intelligence is actively utilized, continuously validated, and expanded to deliver measurable business impact.
What You’ll Do
1. Enterprise BI Strategy & Technical Delivery
Design, deploy, and personally build site-wide BI dashboards, analytics pipelines, and KPI frameworks that support operational, quality, and executive decision-making Analyze plant data to proactively identify operational opportunities (yield optimization, cycle time reduction, energy efficiency, quality improvements) and partner with business functions to translate insights into action
- Co-design data visuals and analytics solutions with Operations, Quality, Engineering, and Warehouse teams, ensuring relevance and usability
- Establish and manage analytics time-to-insight standards, reducing lead time for operational reports and batch disposition analytics
2. Artificial Intelligence & Predictive Analytics
- Lead the execution of 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 reduction through predictive AI models
- Ensure AI model validation and governance align with GxP requirements
3. Data Governance & GxP Compliance Leadership
- Establish and enforce site-wide data governance policies, data lineage protocols, and data quality assurance mechanisms for all non-ERP data sources (SCADA, MES, LIMS, historian systems, IoT platforms)
- Ensure compliance with FDA 21 CFR Part 11 and ALCOA+ principles across data operations
- Lead data quality assurance mechanisms and maintain 100% adherence to GxP/ALCOA+ standards
- Partner with Quality Assurance and Compliance teams to align data integrity protocols and AI validation strategies
- Establish data governance norms for the site, defining standards for data collection, storage, access, and usage
4. Data Architecture & Integration
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 that ensure scalability, security, and regulatory compliance
- Monitor data integration completeness and drive continuous improvement in system connectivity
5. Team Leadership & Development
- Build, mentor, and lead a high-performing team of data engineers, BI developers, data scientists, and data governance specialists
- Foster a culture of technical excellence, continuous learning, and cross-functional collaboration
- Provide hands-on technical mentoring and code reviews; model best practices in design, analysis, and execution
- Develop team members' technical and leadership capabilities through coaching and growth opportunities
6. Value Tracking & Business Growth
- Establish clear KPIs to quantify business value across BI, AI, and Digital Twin initiatives (e.g., reduction in batch cycle time, lower deviation rates, reduced energy consumption, improved OEE)
- Regularly report metrics and ROI to site leadership
- Proactively scout new manufacturing use cases, ROI opportunities, and continuous improvement projects to expand the Digital Twin's footprint and analytics capabilities
Who You Are
- B.S. or M.S. in Chemical Engineering, Mechanical Engineering, Data Science, Computer Science, Industrial Automation, or a related technical field
- 8+ years of experience in pharmaceutical manufacturing, process engineering, automation, data analytics, or industrial digital transformation
- Proven hands-on experience designing and building BI dashboards, analytics solutions, and data pipelines (SQL, Python/R, or equivalent)
Hands-on experience with simulation, Digital Twin solutions, or advanced data modeling—demonstrating ability to drive insights and operational improvements Deep understanding of pharma plant operations (GMP, batch process control, PAT) paired with familiarity in data architectures (IoT, MES, SCADA, historian systems, simulation software like AspenTech, Ansys, or Siemens)
Product & Program Management:
- Proven track record in managing product lifecycles, backlogs, and use case prioritization with demonstrated ability to translate complex business requirements into prioritized, actionable deliverables
- Strong background in building business cases and demonstrating value realization through data-driven metrics
Data Governance & Compliance:
- 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 of ERP systems
Leadership & Stakeholder Management:
Demonstrated success in cross-functional stakeholder management, effectively bridging communication between technical teams, operations, engineering, and quality functions Ability to lead, mentor, and develop high-performing technical teams
- Experience driving organizational adoption of new technologies and methodologies Certifications (Preferred):
- Agile/Scrum Product Owner (CSPO/PSPO)
- Lean Six Sigma (Green or Black Belt)
- Project Management Professional (PMP)
- GxP/ALCOA+ compliance certification or training
Relocation assistance is available for this role.
The expected salary range for this position based on the primary location for this position of Holly Springs, North Carolina is $133,400 - $247,800. Actual pay will be determined based on experience, level, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance.
Link to Benefits
Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.
If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.
Salary Context
This $133K-$247K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Genentech, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($190K) sits 10% below the category median. Disclosed range: $133K to $247K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Genentech AI Hiring
Genentech has 12 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Product Manager. Positions span South San Francisco, CA, San Francisco, CA, US, New York, NY, US. Compensation range: $247K - $312K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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