A look at how to land a job as a data scientist at Tesla

by Vivek Kumar

March 23, 2021

What does being a data scientist at Tesla mean and what skills are required?

Tesla does not need to be introduced. It is one of the biggest automotive giants in the world making electric cars. The company is trying to push the boundaries of electric car technology and has pushed other automakers to switch to electric. Tesla relies heavily on massive amounts of data to improve autopilot, optimize hardware designs, proactively detect faults, and increase the load on the power grid. As Tesla uses a lot of data, it offers great opportunities in data science combined with AI autopilot software, making it a great place for data scientists. Find out what it means to be a data scientist at Tesla?

According to James Wong, personnel reliability engineer at Tesla, data scientists are helping establish visualization tools to facilitate their analysis. For example, data from endurance testing of drive units is uploaded to a database where important metrics are extracted. Any engineer will then be able to research this data and compare the performance of different drive unit designs, all in very neat visualizations. He further noted that each test can involve gigabytes of data and that it would be difficult to analyze the raw data. The database also helps them understand how a unit’s performance degrades over time. James wrote this on Quora.

Elon Musk, Founder and CEO of Tesla, launched the company with a mission to accelerate the advent of sustainable transportation. The company has taken a unique approach to establishing itself in the market. Its business model is based on direct sales and service, rather than on franchised dealers. Tesla’s business model has also expanded to encompass energy storage systems for homes and businesses.

Interview process for Data Scientist at Tesla

To get a data science job at Tesla, you need to qualify for a three-step interview. The first step is a telephone interview which has two parts. The first part is based on questions including background, work experience, etc., while the second part is based on questions of coding and statistics. The second step is the take-out exam for some specific questions. And the last step is the on-site maintenance.

According to PayScale, the average salary for data scientists at Tesla Motors is $ 114,783 / year.


For the Software Engineer / Data Scientist position, Fleet Analytics, published in Tesla’s career section, an ideal candidate should work with stakeholders to write a vague problem statement, refine the scope of the analysis, and use the results to make informed decisions. . He / she will write reproducible data analyzes on petabytes of data using cutting edge open source technologies. The candidate will also understand and apply the concepts of reliability in their data analysis.

Apart from this, the candidate must:

Summarize and clearly communicate the assumptions and results of the data analysis.

Build data pipelines to promote their ad hoc data analyzes in production dashboards that engineers can rely on.

Design and implement metrics, applications and tools that will empower engineers by allowing them to self-service their information about data.

Work with engineers to stimulate the use of their applications and tools.

Write clean, tested code that can be maintained and extended by other software engineers.

Operate and support their production applications.

Keep abreast of relevant technologies and frameworks and come up with new ones that the team could leverage.

Identify trends, invent new ways of looking at data, and get creative to make improvements to existing and future products.

Give lectures, contribute to open source projects, and advance data science on a global scale.


Strong knowledge of Python, SQL.

Solid foundation in statistics.

Experience creating data visualizations.

Experience writing software in a professional environment.

Excellent verbal and written communication skills.

Strong problem-solving skills to help refine problem statements and understand how to solve them with the data available.

Intelligent but humble, with a penchant for action.

Experience with data science tools such as Pandas, Numpy, R, Matlab, Octave, etc.

Experience building data pipelines, web applications, and ML models in a professional environment.

Experience in continuous integration and continuous development.

Experience in DevOps i.e. Linux, Ansible, Docker, Kubernetes, etc.

Understanding of reliability concepts (Weibull, Lognormal, Exponential, etc.), analysis of life data (or survival) and reliability modeling.

Understanding of distributed computing i.e. how HDFS, Spark and Presto work.

Mastery of Scala.

In conclusion, a data scientist combines skills such as data collection, data mining, data analysis, statistics, programming and business acumen, among others.

Sean N. Ayres