What does it mean to be a data scientist at Tesla and what skills are required?
Tesla needs no introduction. It is one of the largest auto giants in the world manufacturing electric cars. The company tries to push the boundaries of electric car technology and propelled other carmakers to go electric. Tesla relies heavily on massive amounts of data to enhance autopilot, optimize hardware designs, proactively detect faults, and augment load on the electrical grid. As Tesla runs vastly on data, it has great data science opportunities combined with autopilot software AI, making it a great place for data scientists. Read out what does it mean to be a data scientist at Tesla?
According to James Wong, Staff Reliability Engineer at Tesla, the data scientists help establish visualization tools to aid in their analysis. For example, drive unit endurance tests data is uploaded to a database where important metrics are extracted. Any engineer then will be able to look up this data and compare the performance of different drive unit designs to each other, all in very neat visualizations. He further noted that each test can involve gigabytes of data, and it would be a pain 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, who is the founder and CEO of Tesla, launched the company with the mission to expedite the advent of sustainable transport. The company took a unique approach to establish itself in the market. Its business model relies on direct sales and service, instead of franchised dealerships. Tesla’s business model has also extended 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 should qualify for a three-step interview. The first step is a phone interview that has two parts. The first part is based on questions including background, working experience, etc., while the second part is based on the coding and statistical questions. The second step is the take-out exam for some specific questions. And the last step is the on-site interview.
According to PayScale, the average data scientist salary at Tesla Motors is US$114,783/ year.
Responsibilities
For the role of Software Engineer/ Data Scientist, Fleet Analytics, posted on Tesla’s career section, an ideal candidate is required to work with stakeholders to take a vague problem statement, refine the scope of the analysis, and use the results to drive informed decisions. He/she will write reproducible data analysis over petabytes of data using cutting-edge open-source technologies. The candidate will also understand and apply reliability concepts in their data analysis.
Apart from this, the candidate is required to:
• Summarize and clearly communicate data analysis assumptions and results.
• Build data pipelines to promote their ad-hoc data analyses into production dashboards that engineers can rely on.
• Design and implement metrics, applications and tools that will enable engineers by allowing them to self-serve their data insights.
• Work with engineers to drive usage of their applications and tools.
• Write clean and tested code that can be maintained and extended by other software engineers.
• Operate and support their production applications.
• Keep updated on relevant technologies and frameworks and propose new ones that the team could leverage.
• Identify trends, invent new ways of looking at data, and get creative to drive improvements in both existing and future products.
• Give talks, contribute to open source projects, and advance data science on a global scale.
Requirements
• Strong proficiency in Python, SQL.
• Strong foundation in statistics.
• Experience building data visualizations.
• Experience writing software in a professional environment.
• Strong verbal and written communication skills.
• Strong problem-solving skills to help refine problem statements and figure out how to solve them with the available data.
• Smart but humble, with a bias for action.
• Experience with data science tools such as Pandas, Numpy, R, Matlab, Octave, etc.
• Experience in building data pipelines, web applications, and ML models in a professional environment.
• Experience with continuous integration and continuous development.
• Experience in DevOps, i.e., Linux, Ansible, Docker, Kubernetes, etc.
• Understanding of reliability concepts (Weibull, Lognormal, Exponential, etc.), life data (or survival) analysis, and reliability modeling.
• Understanding of distributed computing, i.e., how HDFS, Spark and Presto work.
• Proficient in Scala.
Conclusively, a data scientist combines the skills such as data collection, data extraction, data analysis, statistics, programming, and business acumen, among others.
