Tesla is looking to hire a Data Scientist to contribute to the development
and deployment of specialized manufacturing processes for our cutting-edge
vehicle battery products. In this position, you will have access to data
playgrounds across manufacturing processes and equipment globally and the
opportunity to build and implement innovative data science services,
models, and tools into production use. Battery development is at the heart
of our company, and this is an exciting opportunity to work directly on
the central challenges with electric vehicles – battery cost and
performance. Tesla is a demanding and fast-paced environment where you
will work with a highly motivated team on extremely challenging projects.
What You'll Do
Perform exploratory analyses and correlation studies on process-related
inputs and outputs to facilitate the understanding of process mechanisms
Act as the owner/subject matter expert to collect and analyze data,
identify process improvement opportunities, inform solution spaces, and
drive the evaluation of their effectiveness
Develop and deploy sophisticated algorithms and predictive models using
in-process and post-process data to improve manufacturing process
monitoring/part quality prediction capability
Clearly convey results of data analyses in a way that is digestible by
cross-functional stakeholders to facilitate the rapid development and
deployment of solutions
Standardize the reporting architecture of key manufacturing line
performance metrics (OEE, etc.) across various stations and processes
Determine what data should be collected and how on new manufacturing
process and production lines
Develop frameworks and methodologies for experimental design (A/B test,
RCA, GRRs, etc.)
Research and evaluate external data acquisition & processing software for
process monitoring
What You'll Bring
BS, and/or MS in quantitative fields (Statistics, Data Science, Applied
Mathematics, etc.)
1+ years of experience in manipulating and analyzing complex & high-volume
data from various sources
Proficiency with programming languages such as Python, R
Proficiency with relational and/or non-relational databases
Proficiency with data visualization techniques and tools using Tableau, R
Shiny, Python Dash, Plotly, etc.
Strong mathematical/ statistical knowledge relevant to process control and
monitoring
Experience with experimental design is a plus
Experience working with high-volume manufacturing data streams is a strong
plus
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