Getting a Job in Tech as a Statistics Major

Amanda Wong
9 min readSep 28, 2020
Photo by Andrew Neel on Unsplash

In my sophomore year of college, I hit my mid-college life crisis. You know, the one that’s just as part and parcel for students as the Freshman 15. I entered college with the plan to major in computer science, and so far it was not going well. At all.

At my medium-sized “elite” research university, I was struggling to get into computer science classes due to overwhelming demand. Graduating on time with a computer science degree didn’t look feasible. (Apparently, I was experiencing a phenomena occurring on campuses across the country.)

Besides the logistical concerns were more personal ones. After some soul-searching, I realized I liked calculating posterior probability by hand more than I liked traversing linked lists. However, I worried that declaring a Statistics major would make me a less desirable candidate in the tech industry, where technical aplomb is prized by many. With a torn heart, I submitted a petition to switch my major in Statistics, anxious about my career prospects.

Little did I know (as few sophomores do at the time when everyone is telling them to), things would work out just fine. I’m here to tell you that they will be for you too, no matter where you are in your job search. Despite no computer science degree, I interviewed with FAANG and non-FAANG companies as a college senior and now work with the latest technology at one of those “big tech companies.” I haven’t looked back since.

Although the recruiting bias for the most common roles like software engineers, product managers, and even data scientists is skewed towards those with computer science degrees (as far as one can tell from the Requirements section on job listings), statisticians have a lot to offer. It doesn’t mean that the iron gates of the tech industry are closed to you forever. So what is one to do?

This is probably a good time to note that I took only a handful (3? 4?) of computer science classes during undergrad — and they weren’t particularly fancy advanced ones! Yet in my job, all of my peers have a formal computer science education.

Some might consider me the lucky duck, but I can tell you that getting here was no accident.

Below is a combination of philosophical and practical advice for getting the tech job of your dreams. It is not limited to data science, the field that everyone and their dog thinks of when they hear “statistics,” but is geared towards getting your foot in the door of the tech industry.

This guide will show you not only how to use your major to get a job in the tech industry but how to use it to your advantage.

Photo by Eileen Pan on Unsplash

1. Data science is not your only option.

If only I had a Yakult for every time someone saw my statistics degree and asked me something about data science. I would be Yakult King. Heck, I might even own the entire company. But that’s beside the point.

Just as there are so many career options for someone studying computer science, you have many different roles to pursue. Data science has become a vast field with much hype in recent years. It’s a catch-all title for a vast number of tasks and is not meant for everyone who simply likes data.

Data science is fine and good. However, with the hype around data science, the supply of data scientists is climbing, trending towards an oversupply of applicants. It’s going to take even more to establish yourself as the signal among the noise of fellow candidates.

I didn’t realize the variety of roles available to me until I was approached by recruiters with job listings I had never even heard of. I had never heard of my current role title until a recruiter reached out to me.

If I were to redo the college job search again, I would expand my job search scope beyond data science to have more options available to me.

By expanding your search beyond data science, a highly popular role, you can increase your chance of getting selected from the void known as the application portal.

Statistics majors have become really successful data engineers, product managers, business operations managers, and technical consultants. Do you want to analyze business metrics and leverage insights for strategic recommendations? Do you want to build machine learning models to analyze images and speech? If you’re lucky enough to know with specificity what you want to pursue, that will help you stand out among a pool of generalists and you can skip to the next section.

But what if I don’t know what I want to specialize in?

That’s okay! I had no clue what kind of “data science” or “data analytics” I wanted to do before graduation. If you’re in the same boat, I recommend the following strategies for learning what’s out there and, more importantly, what’s meant for you.

✏️ Collect data on what interests you.

Read your CS department’s course catalog and compile a list of topics and keywords that pique your interest. Take note of what class descriptions and tech topics you gravitate towards.

Check out Hacker News and Medium forums (Technology) to figure out whether there’s a trend in which kinds of posts you click on.

🔍 Learn about the nuances of different roles.

Start out with learning the differences between the most traditional “Statistics jobs” out there: data science, data analytics, and data engineering. These roles sound mind-numbingly similar, but there are important distinctions to learn.

You can apply to one or all of these kinds of roles. (Each strategy has its pros and cons.) Regardless, in the long run, familiarizing yourself with what exactly individuals in these roles do on a daily basis.

Doing so might help narrow your focus in your job search. Additionally, it can help you map out a plan to make yourself a strong candidate for those particular roles.

Which brings me to another tip…

🎚️ Set expectations.

You can only learn so much about a role through its job listing. There’s always more beyond a job post’s ambiguous keywords such as “data-driven” and “cross-functional.”

Check out bloggers, forums (e.g., r/statistics, r/dataengineering), and YouTubers (e.g., Ken Jee, Shalini K, and Tina Huang) to learn “what a day in the life of ___” might look like. There’s more new content every day, with a growing number of niche topics. Since there’s so much to sift through, save some time by exploring short form content and taking note of what you’re interested in.

Additionally, you can do some “professional-stalking.” Find someone in your dream role or company. Look them up and see if they may have a Twitter or blog where they discuss professional interests. You never know, it could be a great conversation starter if you want to learn more from them.

2. Give your brand a shine.

Individuals who work with data are often compelling storytellers. They know how to pull insights from data and make others curious about them. Use this skill to your advantage when crafting the story of who you are as a job-seeker.

Reflect on what makes you an outstanding candidate and, in particular, how your statistics background supplements that. For example, has your Statistics training changed your worldview and how you communicate your findings? What do you bring to projects because of your Statistics background?

The story of how you got to where you are today and where you’re heading tomorrow can be your powerful tools. The more thought you put into it, the more it will pay off because it can add so much valuable character to a one-page resume.

3. Make technical proficiency front and center.

Whether or not your dream role is technical, you’ve gotta prove your chops at the door. Data-related roles often require SQL, Python, and R. Much has been written about the best MOOCs available, so I won’t go into too much detail on them here.

Learning languages is one thing; showing that you do is another. Advice out there differs but in my personal experience, keeping my technical skills and projects, buried at the very bottom of the resume made recruiters think twice about me.

I’ve had recruiters skim my resume in front of me and ask, “What kinds of technical projects have you…” before arriving at the bottom of my resume and walking their question back. “Oh.” Another time, a recruiter asked me whether I’ve even taken technical classes even though the last line of resume showed my experience as a computer science TA.

It was awkward. Imposter syndrome started rearing its ugly head.

Based on these interactions, I learned to assume that recruiters looking to fill any positions that require some technical understanding need to have the evidence that you can hold your own in technical skills. If you have technical skills, it should be one of the first things that recruiters see.

4. FAANG is not everythang.

Many of my friends in the Statistics major worked in labs as research assistants and found working in startups a great way to transition from academia to industry.

Although you might not have a coveted “brand name” of a company on your resume or receive the same formal training as you might with a large tech company, what’s important is gaining professional experience and autonomy from working at a startup and even leadership opportunities that might be hard to come by at other companies.

Contrary to what a majority of the internet may tell you, FAANG is not everything. Check out AngelList to search for startups that are hiring. They may not have an official job posting for the role you want, but it wouldn’t hurt to cold email and ask whether they are taking interns. Having worked at an early-stage startup, I’ve learned that oftentimes the more help, the merrier.

5. Choose media wisely.

As the adage goes, you are what you read. Whether you know the role you want to pursue or are still exploring, staying up to date on the latest topics of conversation in your chosen tech communities will help you in the long-run.

For one, it gets you thinking like someone already in the tech industry. Second, it helps you form opinions. A candidate who is able to form opinions (and back them up with reasons!) is incredibly valuable, especially if the role requires them to deliver insights to key stakeholders.

You’ll learn a lot of topics and will be trusted to give an informed, rational opinion on key decisions. The ability to share an opinion or thought on a topic communicates your passion and you’ll come across as a data-driven, business-oriented person.

Right now, I’m paying attention to Cultural Analytics, The Radical AI podcast, and Emerging Tech Brew. For advice geared towards students, check out the thoughtful Tech Gals and Pain Points & Pull Requests podcasts on Spotify.

6. Step up in a tech community.

There are many ways to contribute to a campus club. There are often roles with different levels of commitment, so you can find one that fits your available bandwidth.

On my campus, I joined a club that developed technical solutions for local nonprofits. Find a club on your campus or in your neighborhood that suits your interest (e.g., tech for government or tech for non-profits or tech for environmental justice). Is there a hackathon you can volunteer at? There are so many niche interests attached to technology. If your college doesn’t offer what you’re interested in, start your own!

Each organization obviously varies, but there are many options to step up as a leader. For example, you can organize the logistics of a technical workshop or work on outreach. Recruiters are interested in leaders who go out of their way to participate in and give back to the technical community. It signals to them that you have a genuine passion for technology. You certainly don’t have to be the club president to prove this.

Parting Thoughts 🌱

If I could go back in time to the moment when I hesitated to submit my form to declare a Statistics major, I would tell myself this: Yes, it is possible to get a job in the tech industry.

It’s going to take hustling and self-advocacy to land the role that you really want. The trade-off, however, is that you can get away with writing less code in undergrad if that’s not really your thing and spend time cultivating your passion.



Amanda Wong

Probably thinking about bagels, critical theory, and my current AI project | Currently: Microsoft