Basic research you must do before a data science interview


Basic research you must do before a data science interview

You applied for and received an interview for a data science role. Congratulations! If you’re like most people though, this is when the thoughts of “how do I really stand out so that I can get the job” kick into high gear. Instead of thinking about it a few times a day when your mind drifts from your other work, these thoughts now consume almost all of your waking hours. You think about it as you get out of bed in the mornings, as you go through your schedule during the day, and finally as you brush your teeth at night. It might even been causing you to stay awake at night because you can’t stop thinking about how to finally get the data science job that allows you way more money than you ever made as a graduate student while at the same time allowing you to play with math, statistics, and computers.

You don’t know if you’ll even be ready for the interview.

You’ve read on hacker news how the ability of the general interviewing population in data science is quite saddening, so you’re more than a teensy bit worried that you could fall into that camp. When you read data scientists’ blogs about what you need to know, your stomach sinks because there always seems to be at least three new technologies whose names you don’t recognize. Not only that, a technique like deep learning seems to be the hot new area and you’ve only barely skimmed the surface of the NIPS papers.

I’m Good Enough, I'm Smart Enough, and Doggone It, People Like Me!

You are sure you have valuable skills and you are worried that making the wrong choice or saying the wrong this in the interview will doom you to a life of misery where you’ll never get a data science job. You’ve gone through enough schooling to know more math and statistics that pretty much the whole world. Yet, this isn’t the whole world that you’ll be chatting to, these are real life data scientists with an academic background as impeccable as yours and, not only that, they’ve also successfully made the jump into data science.

You want to stand out and set yourself apart from the crowd in the interview process

Can you imagine how your life would be like if you aced all the interviews. And got the job working with some truly talented data scientists. Where a few months down the line, you’d hear about how when you interviewed you were lightyears ahead of all of the other people interviewing for this job.

Use your research skills to turn you into a lean, mean, interview preparation machine.

Here’s what you’ll have to research (and will go into further details below):

  1. The company where you are interviewing
  2. The competitors of the company where you are interviewing
  3. The industry the company and competitors live in
  4. Where the company, competitors, and industry are headed
  5. What data is generated, used, and studied by data scientists in these organizations
  6. What tools are used by the data scientists and data engineers
  7. What techniques they use
  8. What goals the data science teams have
  9. What are they (data scientists, groups, company, competitors, industry) missing?
  10. Put yourself in their shoes and figure out what they would ask you
This seems like a bunch of work, but knowing all of these things will help you stand out during your interview.

For the first step - researching the company where you are interviewing, here’s a list of things to do:

  • Read everything on the company’s blog
  • Read every tweet sent out by the company in the last couple of months
  • Read every press release about the company
  • Read all profiles of senior people
  • Read all profiles of data scientists / people whom you might meet during the interview

For the second step - researching the competitors of the company where you are interviewing, here’s a list of things to do:

  • Figure out the top three competitors for the company you are interviewing with
  • Read each company’s blogs
  • Read each company’s tweets
  • Read each company’s press releases
  • Read the profiles of the senior people at each company
  • Read all profiles of data scientists / people whom you might hire away from competitors or who could hire you away from each company

For the third step - researching the industry the company and competitors live in, here’s a list of things to do:

  • Figure out the trade rags/newspapers/journals/blogs for the overall industry and read last few issues
  • Figure out the trade rags/newspapers/journals/blogs for the very specific part of the industry this company exists in
  • Look at conferences and see what topics people have presented on that are related to the industry and your specific company and competitors
  • Read the daily rags/newspapers/journals/blogs everyday heading into the interview

For the fourth step - researching where the company, competitors, and industry are headed, here’s a list of things to do:

  • Develop an opinion of what the biggest challenges are in the industry / sector / company
  • Develop an opinion of what the biggest wins in recent memory have been in the industry / sector / company
  • Develop an opinion of what happens next in the industry / sector / company

For the fifth step - researching what data is generated, used, and studied by data scientists in these organizations, here’s a list of things to do:

  • Read the job description again to see if they tell you what data they use or what business lines this particular data science job will support
  • Read description of the various services and/or products that the company sells to get a sense of where they see their value add
  • Look at any projects / papers / press releases the company has written to see if they mention the data being used
  • Use your imagination to place yourself in the job and come up with what data you’d want to use, what things you’d want to model, what things you’d want to predict, and what things you’d want to better understand.

For the sixth step - researching what tools are used by the data scientists and data engineers, here’s a list of things to do:

  • Read the job description again to really understand what tools they are using and what tools they want to see a background in
  • Read description of the various services and/or products that the company sells to get a sense of how big the company is and therefore how much it might spend on tools
  • Look at any projects / papers / press releases the company has written to see if they mention the tools being used
  • Use your imagination to place yourself in the job and come up with what tools you’d want to use given your own knowledge and experience

For the seventh step - researching what techniques they use, here’s a list of things to do:

  • Read the job description again to really understand what techniques are mentioned either directly or indirectly
  • Look at the list of tools to see if they are technique specific tools (for instance, if they mention Neo4j - then they are working with graph data and will thus be using techniques that are directed towards graph data).
  • Use your imagination to place yourself in the job and come up with what techniques you’d want to use given your own knowledge and experience

For the eighth step - researching what goals the data science teams have, here’s a list of things to do:

  • Come up with a list of reasons why the data science team is starting or expanding while taking into account company, competitors, and industry
  • Read the job description again to see if it’s mentioned why they are hiring and what they’d like you to be able to do
  • Use your imagination and logic to figure out based on the people who are already there what skills they may be missing or want to enhance.

For the ninth step - researching what they (data scientists, groups, company, competitors, industry) are missing, here’s a list of things to do:

  • Look again at data science bloggers, data conferences, and papers to see if there are any new techniques or regions that people are exploring that could potentially be useful to the group you are interviewing with
  • Look at the history of the industry to see what has developed and what were the steps that helped it arrive to where it is today to try to think of where it will go next.

For the tenth step - Put yourself in their shoes and figure out what they would ask you, here’s a list of things to do:

  • Come up with a list of questions you think they will ask you
  • Prepare answers that i) make sense, ii) are related to what the company does, iii) are related to how the company thinks, and iv) aren't terrible
  • Come up with a list of "short problems" / "white board problems" that they could ask given i) what they've done, ii) what they're hiring for, and iii) what they could do in the future
  • Come up with a list of "day long problems" that they could ask given i) what they've done, ii) what they're hiring for, and iii) what they could do in the future
  • Come up with a list of "take home problems" (2-7 day problems) that they could ask given i) what they've done, ii) what they're hiring for, and iii) what they could do in the future
  • Come up with a 1 week, 1 month, 3 month, 6 month, 1 year plan of where you'd want to be and what you'd want to do if you got the job
  • Come up with a very direct list of questions that matter to you about where the data science team is right now and where it is going

You are awesome!

It’s easy to get overwhelmed when preparing for a job interview. Just start at the first step and first bullet point and take the tiniest step forward. Then move on to the next one. You’ll get there in no time. And remember, if this is the job you want, then it’s your job between now and when you have your interview to make sure that you get the job.

Even if you don't get it, the preparation you do ahead of time will make it easier to get a data science job at a similar company, if not their direct competitors. And don't forget, as much as they are interviewing you, you are interviewing them. You are awesome after all, and they need to convince you that it's worth your time to join them. :)

Get to it and good luck!


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