特斯拉数据工程师, 电芯质量及可靠性 Data Engineer, Cell Quality & Field Reliability

任职要求

Strong SQL, Python queries for data analytics, applied knowledge of statistical analysis (like hypothesis testing), time series analysis, working knowledge of reliability statistics such as Weibull Analysis
Experience building optimal ETL data pipelines across structured and unstructured data sources
Strong data visualization skills Tableau/JMP, and Python packages such as seaborn, matplotlib
Working knowledge of Big Data technologies like Hadoop ecosystem (Spark, HDFS, Presto etc.)
Excellent verbal and written communication skills - ability to break down complex technical topics and deliver visual technical presentations (e.g., PowerPoint) to groups of  engineers, scientists, and technicians
B.S/M.S. in Data Science, Data Analytics, Computer Science, Engineering (Industrial Engineering…

工作职责

Description
Data Engineer, Cell Quality & Field Reliability

The Team
The Cell Quality team is a small team within the R&D organization responsible for incoming, production and field reliability.  The group projects focus on mass production of Li-ion cells for all programs, including Model S, X, 3, Y and Energy Products.

The Role
The position is to support daily data analytics activities and process improvements suggestions based on fleet data and known field returns for all cell programs with more focus on Kato cell production build issues. Candidate is expected to lead or support quality focused multidisciplinary cell-related projects, typically involving R&D, Design or Production organization. The engineer must be extremely organized, detail orientated, with strong ability to prioritize and multitask, successfully collaborate on projects with a range of business objectives. This person must exhibit the knowledge, leadership, and drive needed to not only challenge the status quo, but also define and execute the optimal path forward.

Responsibilities
Perform extensive data study on all field failures and correlation studies with upstream cell manufacturing process. Create test models to ensure proper detection and outlier rejection criteria for field rejects, especially from Kato production.
Identify trends from field return data and quantify reliability risks for diagnostics of key critical signals/metrics.
Create data visualizations to communicate analysis results with cross-functional teams and drive decision making of key failure modes on field.
Analyze Field Reliability/Quality data for cell related failures and failure modes to predict expected failure rates, affected populations, verify effectiveness of the corrective actions at Tesla and at suppliers.
Provide data integration and setup quality systems for new in-house manufacturing lines and mass production cell models.
Produce cogent and intelligible data visualizations, author technical presentations and summarize high-impact technical findings with strong data analysis package.