Everyone thinks you need to be a perfect data candidate to land a data job at company like Tesla. The reality is more surprising. I recently stat down with my Accelerator student, Lily BL, who landed a data analyst role at Tesla. I asked her what it really takes to land a data role at a company like Tesla and what the day-to-day work looks like once you’re there.

Working as a Data Analyst at Tesla

Tesla obviously has cutting-edge AI model and complex machine learning algorithms. They taught cars to drive themselves and built robots that can serve you dinner. But it’s important to remember, there are other aspects of the company that don’t have or even need such top-level technology. They actually just need someone that who can untangle their data mess.

That became Lilly’s job. She “stitched” together disparate (fancy data word for “not connected”) data sources across systems that didn’t talk to each other (project management data in Jira, hardware-produced logs, and lots of Excel-driven records). Her job became the “glue” that gave managers a bird’s-eye view of team performance. Basically she gave Tesla managers "supervision goggles" that allowed them to know how their org was doing by building a color-coded dashboard system. Green meant don't worry. Yellow meant keep an eye on it. Red meant investigate immediately.

The dashboard saved a lot of time, money, and sanity for the leadership team.

You Don't Need to Know Everything to Start

Although from her studies (including The Accelerator), Lily was a competent data analyst, she still didn’t know it all. And that’s okay. It’s impossible for anyone to know it all. Heck, I definitely don’t know everything in the data world still. But she knew enough to get started, and she knew she could figure stuff out.

"The scary part was like, I don't know. And the exhilarating part was, but I can figure it out.”

She tried to focus less on advanced SQL queries or complex Excel formulas and more on translating business problems into data solutions. She almost considers it “speaking another language”. A skill to be practiced.

You should steal this approach. When learning data, focus on the underlying data process rather than mastering every button in a single product. Best way to do that? Projects.

What Really Gets You Noticed by Recruiters?

Lily must be a SQL genius to land a job at a company like Tesla, right? She must have blown their socks off in the technical interview? Well, maybe. But really (as for 97% of all candidates), it wasn’t her technical skills that got her hired. It was the P & the N of the SPN Method - Project and Networking.

Lily was involved in the data community and had a optimized her LinkedIn in the Accelerator so that way when a Tesla recruiter viewed her profile and saw her portfolio projects, they were intrigued and reached out for an interview. Her advice?

If you're constantly posting your projects and how you solve the problem, [recruiters] will naturally gravitate to you.”

Do you want recruiters to “gravitate” towards you? If so, let me ask you, are you “constantly posting projects”? Or are you stuck not even knowing where to start?

It’s honestly the difference in a good candidate to a “we need to interview her” candidate.

Which do you wanna be? Start posting projects!

And if you’re stuck not even knowing where to start, that’s why I built The Accelerator.