KLA Product Engineer – Reliability Modeling & Statistics 

United States, Michigan, Ann Arbor 

429416621

Job Scope and Complexity:

• Initially understand reliability models, then gradually own and run them to generate insights
• Identify gaps between models and real-world scenarios, and develop innovative ideas to bridge these gaps with clever assumptions
• Write code as necessary to develop a fundamental understanding of reliability and statistical problems, and present insights and visualizations concisely
• Collaborate with cross-functional groups to obtain extensive data from the field and validate model results against field results
• Continuously drive innovation and features in the reliability, availability, and maintainability modeling domain
• Deliver written periodic reports to keep the wider team informed about status and progress
• Present ideas and results in concise decks and summary documents
• Lead cross-functional technical groups, convey key ideas, resolve differing viewpoints with a collaborative spirit, and move discussions forward
• Eager to learn and adept at picking up new technical concepts and software
• Be self-driven with a drive for problem-solving

Preferred Qualifications:

• Demonstrated experience working with any commercial reliability software (e.g., ReliaSoft)
• Demonstrated experience with part level and system level reliability analysis. Experience with reliability analysis of complex equipment systems a plus.
• Experience with failure mode analysis
• Familiarity with SQL and PBI
• Demonstrated experience using statistical and visualization package(s) for problem solving (e.g., JMP, Matlab, R, Python, PBI, Tableau, etc.). Familiarity with SQL is a plus.
• Master’s degree in related discipline is preferred
• Familiarity in the framework of a Product Life Cycle and SEMI E10 definitions is a plus

Minimum Qualifications

• Master's Level Degree and 0 years related work experience; Bachelor's Level Degree and related work experience of 2 years
• Demonstrated experience of advancing solutions to technical problems in a cross-functional environment
• Evidence of learning new software and adaptability to continuously learn new technical topics
• Evidence of applying statistics in solving real world industrial problems. A strong understanding of statistical distributions and their applications in the real world (e.g., Weibull, exponential, etc.)
• A strong grasp of basic probability concepts. An understanding of Bayes’ probability framework is considered a plus.
• Evidence of proficiency in coding in solving real world industrial problems
• Strong interpersonal and communication skills

or at +1-408-352-2808