How To Ace a Data Science Interview: 5 Tips From Data Scientists at Facebook, Uber, Twitter, and Amazon

Guest ContributorGuest Contributor | 5 minute read | May 24, 2021
How To Ace a Data Science Interview

Interviewing for a job as a data scientist can be a nerve-wracking experience. Between compiling a portfolio, completing technical screens and challenges, and going through rigorous interview loops, candidates are often put through the wringer as companies try to determine whether a candidate has the creativity, technical chops, and right attitude to join their data science and machine learning teams.

Fortunately, many data scientists have been through the interview process and shared their experiences both as hiring managers and job applicants. Read on for insights and advice from data scientists at Uber, Facebook, Twitter, Amazon, and Reddit.

How To Ace a Data Science Interview

  1. Stay on top of the latest trends and tools

  2. Advocate for yourself

  3. Have the right mindset

  4. Learn to communicate your strengths

  5. Get your Kaggle on

Tip 1: Stay on top of the latest trends and tools

It doesn’t matter if you’re fresh out of school or have years of experience—it’s important to stay on top of what’s fresh and new in the data science industry, according to Shae Wang, a data scientist at Uber.

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“The things you learn in school are all printed on a textbook, and a lot of times, they’re outdated,” Wang said. “In the workforce, you realize that people are using new methods, they’re using experimental methods.”

In order to stay competitive and to show hiring managers that you’re a self-starter who can keep up with the latest, Wang recommends reading new research papers as they’re published and being aware of the new types of algorithms that are being used.

“Stay on top of [the] news and stay informed,” she said. “Go to conferences and meet people because data science is not a super mature field—there are many opportunities for improvement. You should never be afraid to try new algorithms yourself or even try to develop a new method.”

Tip 2: Advocate for yourself

Many companies place a premium on credentials, which can make it challenging for newcomers who lack experience or a graduate degree in data science to break in. While this is changing, Katie Bauer, a former Reddit data scientist who is now at Twitter, said applicants shouldn’t be afraid to advocate for themselves, especially if they think their skills are transferable.

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“I had to be very aggressive in my applications,” said Bauer, who in her previous career was an analyst. “I would apply for things [that were like], I don’t even know if I’m qualified for this; I don’t even know if I want to do this; but I will see what happens by applying. And I would have recruiters reach out with other analyst positions and I’d be like, ‘I’m overqualified for that. I want to go into a data science position.’ And eventually, it worked. It took a lot of persistence and I was frustrated a lot, but eventually, I got someone to give me a shot. And now that I’ve been a data scientist, it doesn’t matter. The work experience matters a lot more than a background in any particular field. And I think that’s true really for any job in tech. People want to know if you can do the job.”

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Tip 3: Have the right mindset

When companies are interviewing data scientists, they are looking for people who are both skilled individuals and have the mindset to help the organization meet its goals.

“One of the key things that I personally look for is people who have a keen focus on the consumer experience, or the end-user experience that is created by their algorithms, and not necessarily how cool or cutting edge the algorithm itself is,” said Pallav Agrawal, a data scientist formerly at Levi’s who now works at Amazon.

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“Steve Jobs actually has this quote…’You have to start with the consumer experience and then work backward through the technology.’ This is very prescient when you think about how a lot of people who come into the data science from research labs or from being Kaggle Grandmasters focus a lot of their time and energy on the accuracy of the models or the cleanliness of the data, but not necessarily on what would a human feel when they are receiving the output of this algorithm.”

Tip 4: Learn to communicate your strengths

Not all data scientists work in the same ways, and not every data scientist specializes in the same areas. It’s important to be able to identify and articulate to hiring managers what kind of data scientist you are, where your strengths lie, what you could bring to a team, and the type of role in which you would most thrive.

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“Data science is both an art and a science, and I do think that people tend to fall on a spectrum,” according to Instagram data scientist Mansha Mahtani. “Some people tend to be really good at communication, really good at storytelling and the product intuition part of things, and then there are some people that really prefer working on optimization problems, really prefer building those statistical models and really getting into the math of it. And my recommendation would be to figure out where you land and then make sure that you develop some way to communicate that when you are looking for a role or a job. Once you do that, you really stand out amongst your peers because every employer knows that there is a spectrum and it would just be helpful if you were able to communicate your strengths and play those up.

Tip 5: Get your Kaggle on

For job applicants who lack formal professional experience, Kaggle is an invaluable tool for building a portfolio and getting as much practice as possible with wrangling datasets, according to Facebook data scientist Jeevan Mokkala.

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“If you’re new to data science and looking to add some stuff to your resume, I think Kaggle is a great tool,” he said. “ You don’t have to do a machine learning problem, although those are there—you can just do exploratory analysis on their datasets, and they have so many datasets that you can find something that you have some domain knowledge in and you’re interested in. From there, make a couple of notebooks, put it on your resume, and try to showcase different techniques in them.”

Mokkala also suggests working backward from job listings—if a particular role is seeking data scientists with certain skills or experience, a candidate can practice those skills in Kaggle, complete a relevant project, and include it in their portfolio.

Further reading: here is a guide with data science interview preparation tips to know what to expect from your data science interview

Since you’re here…
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This post was written by Tracey Lien.

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