Global Product Support Manager

To make electronics, you need chips, wafers, transistors, reticles, and... To make these, you must see, test and manufacture them at scale-faster and better than ever before. That's where KLA comes in. Whether you're early in your career or an experienced professional, you'll solve complex challenges, work alongside brilliant minds and help shape the future of technology. Group/Division The KLA Services team consists of Service Sales, Marketing, Spares Supply Chain Management, Field Operations, Engineering, Product Training, Digital Solutions and Analytics, and Technical Product Support. Our services organization maximizes the value of our customers' KLA assets - with highly trained service and product support engineers providing installation services and 24/7 technical support and parts delivery through our extensive supply chain network. What You'll Do In this role, you will play a key part in advancing business priorities by delivering high-impact work across your area of expertise. Preferred Qualifications The Product Service Manager owns the customer experience and unit economics of a KLA product family across its install-base lifetime - from new-product field readiness through mature-fleet sustaining. You are accountable for Cost of Service, customer satisfaction, escalation governance, fleet availability, fleet performance, and field readiness for new product introductions. You translate field signal into product, operating-model, and commercial decisions - and hold the line on those decisions across NPI, Sustaining, Tech Support, and Engineering peers. You operate through influence, data, and crisp escalation governance. What you'll own: Customer experience & escalation governance Drive L0 L4 escalation flow from local CSE through PSO Tech Support, PSO Engineering, and Manufacturing keep field cases moving and prevent escalation drift Own the customer-facing voice for service quality across your product family including Quarterly Alignment Meetings (QAM), customer Field Reviews, and recurring customer-of-record cadences (Intel, TSMC, Samsung, Hynix, Micron, and other top fabs Hold the line on routing discipline: case path, distribution-list governance, "right team on the right problem" Cost of Service & financial performance Set and defend per-product Cost of Service targets - by product, region, and division Drive Sustaining-side Continuous Improvement Programs (CIPs) and FCO planning to bend the CoS curve down Own the labor mix (cross-region travel vs. same-region support) and TSE utilization targets Fleet availability Own product-family availability commitments: MTBF, MTTR, escalation duration, L4 frequency Use field data - FSR narratives, escalation patterns, parts consumption, fleet hot-spots - to identify systemic risks before they become customer escalations Partner with Cross-collaboration services teams and Division team root-cause analysis, retrofit programs, and service support strategy Fleet performance Own product-family performance commitments: throughput, cleanliness, repeatability Translate fab-outcome expectations into product-level service deliverables Partner with Division GMs whose tools host your product family - your service performance is their tool's uptime New product field readiness Own field readiness for every new product introduction across the family - training plan, documentation strategy, FRU planning, tooling, hedge kits, beta support, RSL Gate Phase Exits acceptance: formally accept (or refuse to accept) products from NPI into Volume and Sustaining when readiness streams aren't closed Coordinate readiness across multiple concurrent NPI programs - typical PSM scope runs 3-5 NPI programs in parallel Operating model & coverage Set Tech Support coverage strategy by region - including hire plans, regional gaps, and follow-the-sun structure Drive Division Self-Sufficiency: shape the operating model so scarce specialists are backline support, not first-line Run the Quarterly Alignment Meetings and Business Review cadence with action tracking and lookback discipline Preferred Qualifications: Wafer-handler and/or server / data-system product experience - direct hands-on or service ownership in either domain. Cross-product and platform experience is a strong plus. Comfortable with modern AI tooling - actively uses LLM-based copilots, agentic systems, or vector search in day-to-day decision flow. Knows how to ground AI output in source data and how to spot fabrication. Treats AI as leverage Data and analytics fluency - comfort with Snowflake, Power BI, Streamlit, or equivalent. Can interrogate data directly without depending on a downstream analyst for every question. Working knowledge of SQL, Python, or R preferred. Comfortable distinguishing correlation from causation, identifying sample bias, and interpreting fleet-pattern signals. Knowledge engineering mindset - believes institutional knowledge should be captured, indexed, and queryable. Contributes to #J-18808-Ljbffr KLA