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Book: A Guide To Data Science Interviews

About the book

You’re convinced that you want to enter into a data science career. You’ve done your research and even started to learn some of the skills needed. But how do you go from an data science enthusiast to a data scientist at your dream company?

What does a data science interview look like? What do recruiters really think of your resume? Where are the data science jobs? Can you improve your odds of getting an interview by employing a few clever tactics? What questions should you prepare for? These are some of the questions we look to answer in this book. 

What’s inside? 

90 pages of original research, interviews with real data scientists and hiring managers at some of the best data science teams on earth, as well as recruiters and successful candidates who are now data scientists, and actionable checklists. We’ll walk you, step-by-step through everything you need to know to ace the data science interview. 

  • You’ll start by understanding the different roles and industries within data science so you can apply for jobs that are the best fit for you.
  • Next, you’ll learn how to apply for these jobs to maximize your chances of getting an interview.
  • Then, you’ll go over every step of the data science interview process so that you can prepare for what’s coming.
  • Next, you’ll get free sample questions that cover the categories of questions you can expect to receive, which you can use to practice how you approach the data science interview.
  • Then, you’ll get advice on what to do after the interview to move the process forward.
  • Finally, you’ll know what to do if you’re juggling between different offers.

Who is this for? 

Anyone who wants to get a job in data science and anticipates going through a data science interview process. Ideally, you’ve already read our guide to data science careersand are working on building your skills and profiles for a data science interview.

About the authors 

Roger Huang has always been inspired to learn more. He broke into a career in data by analyzing $700m worth of sales for a major pharmaceutical company. He has written for Entrepreneur, TechCrunch, The Next Web, VentureBeat, and Techvibes.

Sri Kanajan is currently a senior data scientist in New York City at a major investment bank. He has 14 years of experience in various engineering and management capacities and made a career transition to be a data scientist in 2013. He completed a full time data science bootcamp in San Francisco and progressed to become a data scientist at two startups and eventually a data science manager at Change.org before taking on his current role. Sri also teaches part time as a lead instructor in General Assembly’s Data Science course. He is passionate about helping others make the transition into data science.


Table of Contents:

What is Data Science?
Different Roles within Data Science
How Different Companies Think About Data Science:
  1. Early­stage startups (200 employees or fewer) looking to build a data product
  2. Early­stage startups (200 employees or fewer) looking to take advantage of their data
  3. Mid­size and large Fortune 500 companies who are looking to take advantage of their data
  4. Large technology companies with well­ established data teams
Industries that employ Data Scientists
Getting a Data Science Interview
Nine Paths to a Data Science Interview
Traditional Paths to Job Interviews:
  1. Data Science Job Boards and Standard Job Applications
  2. Work with a Recruiter
  3. Go to Job Fairs
Proactive Paths to Job Interviews:
  1. Attend or Organize a Data Science Event
  2. Freelance and Build a Portfolio
  3. Get Involved in Open Data and Open Source
  4. Participate in Data Science Competitions
  5. Ask for Coffees, do Informational Interviews
  6. Attend Data Hackathons
Working with Recruiters
  1. How to Apply
  2. CV vs LinkedIn
  3. Cover Letter vs Email
  4. How to get References and Your Network to Work for You
  5. Preparing for the Interview
What to Expect:
  1. The Phone Screen
  2. Take­home Assignment
  3. Phone Call with a Hiring Manager
  4. On­site Interview with a Hiring Manager
  5. Technical Challenge
  6. Interview with an Executive
What a data scientist is being evaluated on
  1. The Categories of Data Science Questions
  2. Behavioral Questions
  3. Mathematics Questions
  4. Statistics Questions
  5. Scenario Questions
  6. Tackling the Interview
  7. Conclusion
What Hiring Managers are Looking For:
  1. Interview with Will Kurt (Quick Sprout)
  2. Interview with Matt Fornito (OpsVision Solutions)
  3. Interview with Andrew Maguire (PMC/Google/Accenture)
  4. Interview with Hristo Gyoshev (MasterClass)
  5. Conclusion
How Successful Interviewees Made It:
  1. Sara Weinstein
  2. Niraj Sheth
  3. Sdrjan Santic
  4. Conclusion
7 Things to Do After The Interview:
  1. Send a follow­up thank you note
  2. Send them thoughts on something they brought up in the interview
  3. Send relevant work/homework to the employer
  4. Keep in touch, the right way
  5. Leverage connections
  6. Accept any rejection with professionalism
  7. Keep up hope
The Offer Process
  1. Handling Offers
  2. Company Culture
  3. Team
  4. Location
  5. Negotiating Your Salary
  6. Facts and Figures
  7. Taking the Offer to the Best First Day


  1. Reaching out to get a referral
  2. Following up after an interview
The book is available, here

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