A Day in the Life of a Tesla Financial Analyst (Bay Area)

Hey everyone, my name is Michael and today I'm going to break down my day in the life as a financial analyst at Tesla. Let's get started. So to fill you in on my official role, I worked as a lead analyst for Tesla's North America and Pacific sales and service infrastructure finance operation. I know it's a really long title and probably an unnecessarily long title. It was kind of funny how I was given the lead analyst role just because there was only one other analyst in China covering China sales and service and one analyst in Europe covering Europe. So I sort of became the lead analyst just because I was covering the main North America portfolio. In terms of job specifics, I pretty much covered the real estate operating expenses and capital expenditures for anything sales or service related. So think your sales stores, your service repair shops, body repair shops, and those kinds of properties. In terms of geographic coverage, I did the main North America portfolio, which at the time was about 300 plus locations in the US, Canada, and Mexico, as well as the Pacific portfolio, which included countries like Japan, Korea, New Zealand, and Australia. All right, so now let's go through my typical work schedule. So I would wake up at around 7 if I was working from home or 6.30 if I was planning to go into the office, since it took me about 40 minutes to drive through traffic from my place in Mountain View to the main factory office in Fremont, California. And yes, I didn't drive a Tesla. I instead drove this wonderful, reliable, and affordable Honda Civic Sport. If I was working from home, then I'd usually do a quick banana and oatmeal breakfast. But if I was planning to go into the office, then I'd usually just eat the free cereal that they had at the cafeteria. At around 8 a.m., I would typically start work, and I usually wouldn't have a meeting before 8 a.m. unless there was a really big meeting with a lot of people, in which case the only free shared available time was really early in the morning. At this time, I would usually check my email for any important updates or urgent tasks, and I would also look at the updated figures in my financial forecast models. I would usually send out emails to relevant business partners for site-level details and updates, since I normally needed to hear from someone a little closer to the local projects to figure out why certain expenditures were going up or down. Then usually, once a week, we'll have a team meeting in the morning. In my case, our team was really small and lean, with about 6 people total. At the time, I was the most junior on the team, being the only analyst, and then we had two senior analysts, one associate, one senior associate, and one senior manager. In these meetings, the senior manager would usually start by providing important updates and highlights, and then we would each go around and talk about our segment of the business. So someone would talk about new cars, someone would talk about used cars, then service and body repairs, then I'd normally talk about real estate, which usually covered things like new property launches and renovations. Then following the weekly meeting, I would usually have some windows of time to do some project work or hop on additional calls. For my project work, I revamped and maintained two main forecast models. One model for operating expenses, so think things like rent and utilities, and the other for capital expenses, so think construction and equipment for new locations and renovation projects. Every month, I would frequently check for variances, investigate issues, and update the forecast model. My goal here was to not only just update the forecast model to make it more accurate, but to also identify any insightful trends or patterns that could lead to an actionable business decision. For example, I could say something like, hey, I think we're planning to spend quite a lot of money on this new location relative to our typical spend. Are we sure it's not going to be more cost effective to simply expand on an existing location nearby instead? Within this period, I would also have some meetings, so I might tune into the construction team meeting to review updates on current projects, or I might schedule a call with the utilities analyst, for example, to figure out why utility bills have been going up in the past few months. At around 12, I would usually try to take a lunch break at home, or if I was in the office, I'd try to get some lunch with some colleagues if they weren't busy with work. There were usually food trucks parked outside the Fremont factory office, but they were a little pricey and not too filling, so I didn't eat there too often. Usually I'd just hit the microwaves with something basic, so a Tupperware filled with pasta or chicken and rice. Then just after lunch, I would try my best to spend as much time as possible completing my project work. Outside of the two main forecast models that I maintained every month, I also worked on other real estate oriented financial models. For example, I worked on this new location projection model where I would receive planned spend for a new location for their operating expenses and capital expenditures, and then I would have to compile comparable figures based off of comparable locations to help the managers figure out if this was a reasonable project from a financial perspective. My goal here was to automate the comparison in Excel so that the comparable figures were automatically calculated based on variables like property type, size, location, new build versus renovation, and several other factors. In addition to this, I also worked on a capital expenditures budget versus actuals analysis model. Here, the actual model idea and layout wasn't very hard, but the data quality was the most challenging part about this because there were always certain spend items that were being classified incorrectly or placed on the wrong projects. So overall, if you're preparing for a role like this, I'd say data cleaning and data analysis skills are very, very important. Then, at some point in the afternoon, I'd usually have a one-on-one meeting with my senior manager where we talked about anything and everything. At the time, I was doing a lot of work revamping our real estate forecast models, so we often talked about that, but we also talked about other things like requests from other teams or just general career mentorship. Then, at around four to five, I'd try to take a break from the financial models and work on the capital expenditure spend requests that I've been stacking up in my inbox. Here, I was responsible for approving the new spend invoices for new construction and renovation projects. So for example, if a service repair shop in South Korea wanted to spend some money on additional car lifts, then they would submit a capital expenditure spend request to be reviewed and approved by me. This was an eye-opening experience for me because as a fresh college graduate, I didn't expect to be in such an impactful role in the business. On average, I'd say I get about five to 10 spend requests per day, given that we were growing really rapidly with 350 plus locations in North America and the Pacific, and I'd say on my worst day, I had something like 30 spend requests stacked up on my inbox. Yeah. Then, at around 6 p.m., I'd try to have some dinner by throwing something in the oven and having it with some rice or pasta and some veggies. And if I had some extra time, I'd usually try to hit the gym. Then after dinner, I'd usually try to finish up some work and respond to a few emails. Here, I would sometimes spend some time working on some extra reports requested by other teams. So for example, I might put together a report showing the most efficient locations by output or cost per square foot. Sometimes these reports would be a bit of a sensitive or touchy subject because a lot of these metrics were being used to evaluate different real estate project managers. But in my case, I tried to just be as fair and logical as possible. And I really came to learn that it's not always about the plain numbers on paper because there are always some unique business circumstances or business objectives that could explain some outliers in the data. Then after 10 p.m., I'd usually try to take it easy, wind down and relax by watching some TV with some housemates. And I'd usually try to go to bed by around 11 p.m. Alright, so that's my day in the life of a Tesla financial analyst. If you're currently recruiting for a new job, consider checking out this video here. And as always, if you found this video useful, please give us a like and subscribe and stay tuned for more videos.