What Data Science Careers Are Like at Big Companies

The data science field is rich and rewarding – and companies are hiring! 

According to the Bureau of Labor Statistics, the job outlook for data scientists is growing at a rate of 22% in the United States, which is much faster than the average for most other occupations—and that’s no wonder, given the value of data in our lives. 

Data science is integral to new types of technologies that provide us with insights through analyzing vast troves of information. Data science is a big part of the engines that bring us functionality in social media, GPS maps, streaming media and the internet at large. It’s part of every modern convenience and advantage, from online shopping to remote work to gaming.

But what are data science jobs actually like?

Part of learning more about what data scientists do involves looking into these kinds of roles and how they are defined at large tech companies like Facebook, Apple, and Google.

In the U.S. technology market, experts have an acronym for the “big five” tech companies with the biggest markets and most brand recognition. It’s called FAANG and stands for: Facebook, Apple, Amazon, Netflix, and Google. (Soon, the market community will have to change this moniker due to Facebook’s name change to Meta!)

Showing how these big companies handle data science work, we can learn more about other programs, such as how The Data Incubator (TDI) prepares you for that future.

Read this article about how TDI made the cut as one of the legit bootcamps to get you hired at FAANG.

Life as a Data Scientist

Data science is a growing industry. Much like the technology it powers, the job title hasn’t been around for more than a few decades. In 2016, there were just 1,700 data scientist positions. Six years later, job openings have soared more than 480%, and the numbers are continuing to climb. In 2021, there were more than 10,000 data scientist jobs. 

However, there’s something even more impressive about data science careers that will delight people heading this way —it’s the satisfaction ratings attached to these jobs! Glassdoor has listed ‘data scientist’ as the third-most-favored job in America in 2022; it has a satisfaction rating of 4.1/5.

Data scientists work at companies of all sizes. While you might associate the need for data science with FAANG companies (namely Facebook, Amazon, Apple, Netflix and Google/Alphabet, as mentioned above), the reality is that data professionals are needed at companies of all sizes, and in most industries. Companies from Intel and IBM to retail companies and social media networks are investing in data science so they can make better predictions for and about their customers.

Comparing Data Science Jobs at Small Companies vs. Large Ones

If you’re starting a career in data science, it can be challenging to know what to expect. While every company is different, there are some general distinctions between smaller companies and larger ones that career pros should consider. Look carefully at the pros and cons for each type of company, then use that information to guide the search for your next position.

Small Companies

Smaller companies will have different data science needs than larger ones. 

Data scientists at startups or on other smaller teams might be tasked with a variety of job functions, related to wrangling data in different ways. Smaller companies tend to have lower pay and fewer benefits, but they often support greater work-life balance, and sometimes that flexibility can make up for the disparity. 

For instance, a smaller company may pay you a little less than a larger one can afford, but you might have unlimited time off, or the ability to work from home. Chances for career growth can also be strong if the company is growing. Startups frequently fall into this category.

Large Corporations

Large corporations generally offer higher base salaries than smaller companies, and they have a greater range of benefits and perks. However, there is a trade-off. The hours can be long, and it may be challenging to maintain a work-life balance, especially in the beginning. That said, some people prefer to make that trade. In exchange, they generally get more pay, in addition to more structure in the job role. 

In these big companies, career growth paths tend to be clearly defined, as are the roles and responsibilities of each staff member. Larger corporations may also be able to provide their data science professionals with more resources.

Data Science Career Benefits

Here’s a sample of some of the benefits that employees at FAANG companies receive:

Note: This was made using the Benefit Comparison Tool at Levels.fyi.

Data Science Roles and Responsibilities

Working as a data scientist at a FAANG firm looks similar to some of the biggest companies. Candidates generally have an advanced degree in a STEM discipline, as well as documented experience in programming, machine learning, data mining, and data analysis.

A certification from TDI checks the box. Through our data scientist programs, we can help you get the education and experience you need to be competitive in the data science industry. Apply today to get started on a career you’ll love.

People with data backgrounds may qualify for a number of data science jobs at these companies. While the names of the jobs might vary a little between FAANG companies, the data science roles they offer typically fall into one of four categories:

Data Analyst

Data analysts look at data to identify opportunities for improvements. They define metrics and build reporting solutions. Many data analysts work closely with other departments, like business or operations, in order to make meaningful conclusions about the trends they identify.

Machine Learning Scientist

Machine learning scientist roles take data analysis a bit deeper. They use machine learning, in addition to standard data analysis, to uncover even more conclusions about how the data is trending and what it means.

Applied Data Scientist

Applied data scientists pull from data analysis and machine learning to build models and deliver scalable solutions. Statistics are a big part of this role. Candidates will also need a strong understanding of model development, data validation, and automated data systems.

Data Engineer

Data engineers build tools. They need to develop data architecture, pipelines, and infrastructure. While the other data science careers we have discussed so far fall squarely under data science, data engineering is a little different. To this end, TDI offers a Data Science & Engineering Fellowship that centers on the needs for this job function.

Understanding the Application Process

From Amazon to Google, the process of applying to any FAANG company is going to be similar. Candidates start by applying online. You generally set up a profile that you can use to apply to different jobs. Because there are so many applicants, candidates are encouraged to reapply and apply to similar jobs. Timing is everything at this step in the hiring process.

If a member of the team thinks you would be a good fit for the role, you will start the interview process. Unlike many other industries, the data science interview process can be a little all over the place, and it is rarely straightforward. The exact process will depend on the company, the department, and the position. Expect to take an online assessment, have several phone or video conversations, and conduct some small project work—all before you get to the more in-depth interview.

The in-depth interview (or interviews) are generally conducted in person or over video. In some cases, you will have multiple interviews on the same day. For instance, Google typically has candidates participate in three to four interviews in a single day. The interviews are highly structured in an attempt to assess all candidates in the same way so they can be compared more readily. Remember, everyone who reaches this point is qualified for the role. The question is whether they would be a good fit for that role as part of that team.

Work Environments at a Larger Data Science Company

Work environments in larger corporations like FAANG tend to be organized into smaller teams that are part of a larger group. Amazon is a good example of this. It has a two-pizza rule. The idea is that the most effective teams are small enough that it only takes two pizzas to fill them up. The smaller group size is meant to facilitate contribution, but another advantage is that smaller teams have fewer politics.

Work-life balance can be a struggle at big companies. The stakes are high, and teams are under pressure to deliver. Amazon’s Jeff Bezos describes work-life as a circle instead of a balance. People who are happy at work tend to be happy at home and vice versa. FAANG companies understand that the demands are high, so they give all the pay and perks needed for employees to live their best lives while delivering their objectives.

To Succeed in Data Science, Always Be Learning

Data science is a rewarding career, but it doesn’t follow the standard trajectory of most jobs. The field itself is ever-expanding. 

With that in mind, it makes sense that the most successful data scientists are those who evolve their skill sets and find new ways to apply their talents. Many of the top people in data science careers learn the skills they use every day AFTER graduation. They made the decision to learn new programming languages and ways of data modeling so that they could expand their professional horizons — sometimes in unexpected directions. No one data scientist is an expert in everything, and there will be rejections along the way. The key to success is to always be learning.

Choosing The Data Incubator

While you could take pre-recorded classes to learn new skills, programs like TDI’s Data Science Bootcamp give students the chance to truly level up. They learn in a small classroom environment, with live teachers, so they can ask questions and get feedback in real-time. They get the chance to use real-world data to solve actual business challenges. The result is a portfolio that demands attention in the data science job market.

TDI has a long track record of preparing our graduates for success in data science. On average, our alumni earn a base salary of $124,000—not including additional bonuses. That is more than 16% higher than the industry average in the United States. Almost 90% of TDI students are employed within six months of graduating from our programs, and 80% of those jobs are found through the networks that we help our candidates build.

Click here to learn how TDI supports our alumni and read reviews from program graduates.