✦ Luna Orbit — Data & Analytics

Product Development Engineer (Hardware, Software & Data Analytics)

at KLA

📍 Milpitas, CA Unknown 💰 $117K – $200K USD / year Posted March 13, 2026
Salary $117K – $200K USD / year
Type Not Specified
Experience mid
Exp. Years Not specified
Education Not specified
Category Data & Analytics

This role involves developing data-driven solutions for semiconductor inspection systems, focusing on data analytics, reduction, and predictive modeling to enhance system reliability and performance.

  • Define source data, Develop data reduction strategies, Build analysis pipelines, Apply statistical and predictive methods, Characterize system performance

The technical environment includes data analytics, machine learning, statistical analysis, and pipeline development, primarily working with sensor and high-dimensional data.

The ideal candidate is a mid-level data scientist or data analyst with experience in semiconductor data, skilled in analytics, machine learning, and data pipeline development. They should be capable of translating sensor data into actionable insights to improve system performance.

Data analyticsData reductionPredictive analysisMachine learningStatistical analysis
AIAdvanced analyticsSensor data analysis
PythonRSQLData pipelines
Data analyticsData reductionPredictive analysisMachine learningStatistical analysisSensor dataData pipelinesAI
Data analyticsData reductionPredictive analysisMachine learningStatistical analysisSensor dataHigh-dimensional dataAnalysis pipelines
Problem-solvingAnalytical thinkingCollaborationCommunication
Industry Semiconductor Manufacturing
Job Function Data analytics and system performance improvement through data-driven insights
Data analyticsData reductionPredictive analysisMachine learningStatistical analysisSensor dataAnalysis pipelinesHigh-dimensional dataData-driven system developmentData characterizationFailure modesData pipelinesData analytics toolsPredictive modelingAIData-driven

Lack of experience with semiconductor data, No experience in data analytics or machine learning, Unable to analyze high-dimensional data

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