These are the data science programming languages that Tesla uses!

Welcome back! Well, this Tesla article I wrote the other day went pretty viral, so now let’s go into a little bit more detail of the programming languages that Tesla uses, specifically the languages they use for data science. The process of me finding these languages was extremely complex and complicated, just kidding, I just went to their career page, looked at the data science positions and tallied the programming languages they require:

The Languages

The most popular languages I saw Tesla used for data science positions are: Python, Scala, MATLAB, R and SQL, they also required experience with: Pandas, Numpy, Docker, Kubernetes, distributed computing, Tableau, RShiny, Matplotlib, PowerBI and machine learning packages (sci-kit learn, Tensorflow, etc.). If that’s all you wanted to know then you’re welcome ☺️, otherwise let’s get into some more details of these languages!


Starting off, Python is by far one of the most popular programming languages right now, it’s used for tons of different things, one of these things is data science. Some data science positions were hybrid data science / software engineering positions, Python is a great balance of both of those cases. Most of these positions required experience, under their Nice to have column, with Pandas and Numpy, these are very popular data processing packages with Python:

Below is a section from their career page mentioning experience with Python:


Scala is another pretty popular language focused around object oriented / functional programming. This language is pretty much built off of Java, but it still has some features that maybe better to a Data Scientist than Java. Also, a huge tool that’s used within the Data Science / Data Engineering community is Apache Spark, this tool is built on Scala. Since Tesla also required experience with distributed computing, it’s no surprise they mention Scala as a requirement. Below is a section from a Tesla data science mentioning Scala:


MatLab is another popular programming language that’s used for a lot of different data processing projects. I definitely haven’t created anything super crazy with this language personally (besides some linear interpolation projects), but I do understand the value that this language holds. This language is great for data analysis, algorithm development, building desktop / web apps, and one of my favorite features of this language was coding in the cloud, it allow me to not necessarily have the MATLAB software installed on my personal machine but I was still able to code / compile within a web browser (just like Google Colab). Below is a section from a Tesla data science position mentioning experience with Matlab:


Next up we have SQL, this is technically a query language, but it’s still a very valuable language to learn. This is not a substitute to any other language on this list, you must learn SQL and a combination of other languages on this list. This language essentially allows you to create and manage databases, this is essentially where our data is stored. To keep it simple (and to motivate you to learn this), I would probably say that every single data science position i’ve ever seen has required some knowledge of SQL (or NoSQL, MySQL, etc.). Basically, you have to know SQL in order to become a data scientist, luckily for you, it isn’t extremely hard to learn this language. Below is a snippet from a Tesla data science position mentioning experience with SQL:


Next up we have R, one of my favorite programming languages. This is a statistical language, I personally feel like R is my go to language for Data Science, it does suffer from one specific area, machine learning. Although R has a few machine learning packages (Tensorflow), most of the documentation / tutorials out there rely on Python, so this means that a majority of companies will end up using Python instead of R, should this sway you away from learning R? Absolutely not, many companies still rely on R programmers to process data. Below is a section from Teslas career page mentioning experience with R:

As Always

As always, if you have any suggestions, thoughts or just want to connect, feel free to contact / follow me on Twitter! Also, below is a link to some of my favorite resources for learning programming, Python, R, Data Science, etc.

Thanks so much for reading!

Data Scientist / Engineer

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