Data Engineer, Cell Quality Engineering

Tesla

Responsibilities

  • 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 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.

Requirements

  • 1-2 years of relevant industry experience
  • B.S/M.S. in Data Science, Data Analytics, Computer Science, Engineering (Industrial Engineering, Mechanical Engineering, Materials Science is a plus)
  • 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

    Desirable Attributes

  • Experience to recommend statistical/analytical models based on manufacturing data and process setup changes to improve quality of product
  • Quality control experience in test/manufacturing environment
  • Design of experiments for process optimization