Staff Statistician: Lead Data Analytics for Manufacturing

KLA-Belgium; Milpitas (CA); On-site; USD 186,000 - 273,000; Full time

Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

401(k) matching

Employee stock purchase plan

Job summary

KLA is seeking a Staff Statistician to lead applied statistics and reliability analytics across product, engineering, and service groups in Milpitas, CA. You will shape the enterprise statistical strategy and translate complex data into actionable insights for product and process decisions.

Lead training, design experiments (DOE), and develop models for manufacturing performance. Excellent communication and cross-functional collaboration are essential for success.

Qualifications

• Master’s or Ph.D. in Statistics, Applied Mathematics, Industrial Engineering, or a related quantitative field.
• Minimum 5 years of experience in a statistician or closely related role, preferably in a manufacturing, engineering, or high-tech environment; 8+ years desired.
• Excellent training, communication, presentation, and interpersonal skills.
• Expert user of statistical software (e.g., JMP, Minitab, SPSS, R, or SAS).

Responsibilities

• Define and own the use of statistical methods in the product life cycle.
• Partner with senior engineering and product leaders to align insights with roadmaps and risk decisions.
• Identify new opportunities for statistical methods across areas.
• Create dashboards and software analytics for monitoring manufacturing and field performance.
• Provide training and consulting on statistical methods and data analysis best practices.

Skills

Statistical leadership

Experiment design

Data analysis

Multivariate analysis

Education

Master’s or PhD in Statistics/Applied Mathematics/Industrial Engineering

Tools

JMP

Minitab

SPSS

R

SAS

Job description

KLA is seeking a Staff Statistician to lead applied statistics and reliability analytics across product, engineering, and service groups in Milpitas, CA. You will shape the enterprise statistical strategy and translate complex data into actionable insights for product and process decisions.

Lead training, design experiments (DOE), and develop models for manufacturing performance. Excellent communication and cross-functional collaboration are essential for success.