Should You Join FAANG or a Startup as a Data Scientist? | by Torsten Walbaum | Jun, 2024

Lessons from working at Uber + Meta, a growth stage company and a tiny startup

Torsten Walbaum
Towards Data Science
Image by author (created via Midjourney)

What type of company you join is an incredibly important decision. Even if the company is prestigious and pays you well, if the work environment is not a fit, you’ll burn out eventually.

Many people join a startup or a big tech company without a good understanding of what it’s actually like to work there, and often end up disappointed. In this article, I will cover the key differences based on my experience working at companies ranging from a small 10-person startup to big tech giants like Uber and Meta. Hopefully this will help you decide where you want to go.

If you want to skim the article, I am adding a brief summary (“TL;DR” = “Too long, didn’t read”) at the end of each section (something I learned at Uber).

Think of a tech company you know. Chances are, you thought of Google, Meta, Amazon, Apple or a similar large company.

Based on these companies’ reputation, most people assume that anyone who works there meets a very high bar for excellence. While that’s not necessarily true (more on that below), this so-called “halo effect” can help you. Once you have the “stamp of approval” from a big tech company on your resume, it is much easier to find a job afterwards.

Many companies think: “If that person is good enough to be a Data Scientist at Google, they will be good enough for us. I’m sure Google did their due diligence”.

Coming to the US from Germany, most hiring managers and recruiters didn’t know the companies I used to work for. Once I got a job at Uber, I was flooded with offers, including from companies that had rejected me before.

You might find that unfair, but it’s how the system currently works, and you should consider this when choosing a company to work for.

TL;DR: Working for a prestigious company early in your career can open a lot of doors.

As mentioned above, people often assume that FAANG companies only hire the best and brightest.

In reality, that’s not the case. One thing I learned over the years is that any place in the world has a normal distribution of skill and talent once it reaches a certain size. The distribution might be slightly offset on the X axis, but it’s a normal distribution nonetheless.

Image by author

Many of of the most well-known companies started out being highly selective, but as they grew and ramped up hiring, the level of excellence started reverting to the mean.

Counterintuitively, that means that some small startups have more elite teams than big tech companies because they can afford to hand-pick every single new hire. To be sure, you’ll need to judge the caliber of the people first-hand during the interview process.

TL;DR: You’ll find smart people in both large and small companies; it’s a fallacy that big tech employs higher-caliber people than startups.

How much you’ll earn depends on many factors, including the specific company, the level you’re being offered, how well you negotiate etc.

The main thing to keep in mind: It’s not just about how much you make, but also how volatile and liquid your compensation is. This is affected by the composition of your pay package (salary vs. equity (illiquid private company-stock vs. liquid public company stock)) and the stage of the company.

Here is how you can think about it at a high level:

  • Early-stage: Small startups will offer you lower base salaries and try to make up for that by promising high equity upside. But betting on the equity upside of an early-stage startup is like playing roulette. You might hit it big and never have to work again, but you need to be very lucky; the vast majority of startups fail, and very few turn into unicorns.
  • Big Tech: Compensation in big tech companies, on the other hand, is more predictable. The base salary is higher (e.g. see the O’Reilly 2016 Data Science Salary Survey) and the equity is typically liquid (i.e. you can sell it as soon as it vests) and less volatile. This is a big advantage since in pre-IPO companies you might have to wait years for your equity to actually be worth something.
  • Growth stage: Growth stage companies can be an interesting compromise; they have a much higher chance of exiting successfully, but your equity still has a lot of upside. If you join 2–3 top-tier growth stage companies over the years, there is a good chance you’ll end up with at least one solid financial outcome. Pay in some of these companies can be very competitive; my compensation actually increased when I moved from Meta to Rippling.

TL;DR: Instead of just focusing on salary, choose the pay package that fits your appetite for risk and liquidity needs.

We all want job security.

We might not stay in a job for our entire career, but at least we want to be able to choose ourselves when we leave.

Startups are inherently riskier than big companies. Is the founder up to the job? Will you be able to raise another round of financing? Most of these risks are existential; in other words, the earlier the stage of the company you join, the more likely it is it won’t exist anymore 6–12 months from now.

Image by author

At companies in later stages, some of these risks have already been eliminated or at least reduced.

In exchange, you’re adding another risk, though: Increased layoff risk. Startups only hire for positions that are business critical since they are strapped for cash. If you get hired, you can be sure they really needed another Data Scientist and there is plenty of work for you to do that is considered central to the startup’s success.

In large companies, though, hiring is often less tightly controlled, so there is a higher risk you’ll be hired into a role that is later deemed “non-essential” and you will be part of sweeping layoffs.

TL;DR: The earlier the company stage, the more risk you take on. But even large companies aren’t “safe” anymore (see: layoffs)

A job at a startup and a large company are very different.

The general rule of thumb is that in earlier-stage companies you’ll have a broader scope. For example, if you join as the first data hire in a startup, you’ll likely act as part Data Engineer, part Data Analyst and part Data Scientist. You’ll need to figure out how to build out the data infrastructure, make data available to business users, define metrics, run experiments, build dashboards, etc.

Your work will also likely range across the entire business, so you might work with Marketing & Sales data one day, and with Customer Support data the next.

In a large company, you’ll have a narrowly defined scope. For example, you might spend most of your time forecasting a certain set of metrics.

The trade-off here is breadth vs. depth & scale: At a startup, your scope is broad, but because you are stretched so thin, you don’t get to go deep on any individual problem. In a large company, you have a narrow scope, but you get to develop deep subject matter expertise in one particular area; if this expertise is in high demand, specializing like this can be a very lucrative path. In addition, anything you do touches millions or even billions of users.

TL;DR: If you want variety, join a startup. If you want to build deep expertise and have impact at scale, join Big Tech. A growth stage company is a good compromise.

When I joined UberEats in 2018, I didn’t get any onboarding. Instead, I was given a set of problems to solve and asked to get going.

If you are used to learning in a structured way, e.g. through lectures in college, this can be off-putting at first. How are you supposed to know how to do this? Where do you even start?

But in my experience, working on a variety of challenging problems is the best way to learn about how a business works and build out your hard and soft skills. For example, coming out of school my SQL was basic at best, but being thrown into the deep end at UberEats forced me to become good at it within weeks.

The major downside of this is that you don’t learn many best practices. What does a best-in-class data infrastructure look like? How do the best companies design their metrics? How do you execute thousands of experiments in a frictionless way while maintaining rigor? Even if you ultimately want to join a startup, seeing what “good” looks like can be helpful so you know what you’re building towards.

In addition, large companies often have formalized training. Where in a startup you have to figure everything out yourself, big tech companies will typically provide sponsored learning and development offerings.

TL;DR: At early-stage companies you learn by figuring things out yourself, at large companies you learn through formal training and absorbing best practices.

We already talked about how working at prestigious companies can help when you’re looking for a new job. But what about your growth within the company?

At an early-stage company, your growth opportunities come as a direct result of the growth of the company. If you join as an early data hire and you and the company are both doing well, it’s likely you’ll get to build out and lead a data team.

Most of the young VPs and C-Level executives you see got there because their careers were accelerated by joining a “rocket ship” company.

There is a big benefit of larger companies, though: You typically have a broader range of career options. You want to work on a different product? No need to leave the company, just switch teams. You want to move to a different city or country? Probably also possible.

TL;DR: Early-stage, high-growth companies offer the biggest growth opportunities (if the company is successful), but large companies provide flexibility.

There are many types of stress. It’s important to figure out which ones you can handle, and which ones are deal-breakers for you.

At fast-growing early-stage companies, the main source of stress comes from:

  • Changing priorities: In order to survive, startups need to adapt. The original plan didn’t work out? Let’s try something else. As a result, you can rarely plan longer than a few weeks ahead.
  • Fast pace: Early-stage companies need to move fast; after all, they need to show enough progress to raise another financing round before they run out of money.
  • Broad scope: As mentioned above, everyone in an early-stage company does a lot of things; it’s easy to feel stretched thin. Most of us in the analytics realm like to do things perfectly, but in a startup you rarely get the chance. If it’s good enough for now, move on to the next thing!

In large companies, stress comes from other factors:

  • Complexity: Larger companies come with a lot of complexity. An often convoluted tech stack, lots of established processes, internal tools etc. that you need to understand and learn to leverage. This can feel overwhelming.
  • Politics: At large companies, it can sometimes feel like you’re spending more time debating swim lanes with other teams than doing actual work.

TL;DR: Not all stress is created equal. You need to figure out what type of stress you can deal with and choose your company accordingly.

There is no one-size-fits-all answer to this question. However, my personal opinion is that it helps to do at least one stint at a reputable big tech company early in your career, if possible.

This way, you will:

  • Get pedigree on your resume that will help you get future jobs
  • See what a high-performing data infrastructure and analytics org at scale looks like
  • Get structured onboarding, coaching and development

This will provide you with a solid foundation, whether you want to stay in big tech or jump into the crazy world of startups.

Working at a small startup, growth stage company or FAANG tech company is not inherently better or worse. Each company stage has its pros and cons; you need to decide for yourself what you value and what environment is the best fit for you.

For more hands-on advice on how to scale your career in data & analytics, consider following me here on Medium, on LinkedIn or on Substack.

Source link

[aisg_get_postavatar size=64]