Manufacturing Engineer - Process Development & Data Science
Sparks, NV

As a Tesla Manufacturing Engineer, you have the important role of designing “The Machine that builds the Machine”. You will work closely with the cross-functional teams (Design, Quality, Production, Sustaining, and Supply Chain) throughout the entire lifecycle of a product (from initial design through prototype development and into full production) to develop high quality and efficient manufacturing equipment and processes that will produce the power electronics that drive our cars and energy products. This will be an extremely hands-on position with both equipment and data.


Process Development & High Volume Manufacturing

Conduct exploratory studies to identify assembly processes to meet challenging product requirements
Identify critical process parameters and conduct experiments using Design of Experiments (DOE) methods to determine sensitivity and acceptable process windows
Develop cause and effect analysis, Process Failure Mode Effects Analysis (PFMEA) and control plan
Troubleshoot and identify corrective measures to critical equipment (mechanical, electrical, thermal, optical etc.) related problems
Conduct installation and qualification of tools using Measurement Systems Analysis (MSA), Process Stability and Capability, and equipment/process matching
Enable Statistical Process Control (SPC) and create standard operation and maintenance procedures to support the transition to sustainable production
Execute data driven root cause analysis as part of reject validation and excursion investigations
Drive continuous improvement & optimization activities to meet production targets including availability, quality, and performance
Data Science

Solve manufacturing challenges (e.g. process capabilities, efficiency, cost) through descriptive, predictive and prescriptive analytics methods
Build data pipelines to enable operational and exploratory analysis
Define and standardize metrics, and implement reports and dashboards using data visualization tools

BS/MS/PhD in Engineering, Physics, Mathematics (or equivalent)
Hands-on experience in building/troubleshooting electromechanical systems and acquiring experimental data
Experience with querying, aggregating, analyzing, and visualizing data
Proficiency with scripting (Python), querying (SQL), and statistical (JMP) languages/programs (or equivalent)
Strong understanding of Statistical Analysis, Design of Experiments (DOE), Gauge Repeatability and Reproducibility (GRR), Failure Mode Effects Analysis (FMEA), Statistical Process Control (SPC) and other Six Sigma Tools
Experience with machine/deep learning techniques are a bonus
Minimum of 1 year experience in an automated manufacturing environment (including internships)
Able to work under pressure while managing competing demands and tight deadlines of multiple simultaneous projects and challenges
Strong problem solving skills and aptitude for learning systems quickly
Communicate effectively with direct team and peers within a manufacturing and engineering organization. This includes exemplary verbal and written communication skills.
Detail oriented with strong record-keeping and organizational skills

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