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      Capital One

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      Principal Data Scientist Interview

      17 Jul 2018
      Anonymous interview candidate
      McLean, VA

      Other Principal Data Scientist interview reviews for Capital One

      Principal Data Scientist Interview

      22 Oct 2025
      Anonymous interview candidate
      No offer
      Neutral experience
      Difficult interview

      Application

      I applied through an employee referral. I interviewed at Capital One

      No offer
      Negative experience
      Easy interview

      Application

      I applied through a recruiter. The process took 3 months. I interviewed at Capital One (McLean, VA) in Apr 2018

      Interview

      A recruiter reached out to me and was very encouraging and excited. The role expected a publication history and capacity to conduct research. The position was nominally up my alley, so I enthusiastically pursued the opportunity. The process was long, but each step was easy. Several stages must be passed before even discussing the real role, which makes the process prone to sinking applicants' time with no payoff. The first step was an easy coding challenge to test coding efficacy. I then had a technical phone interview that occurred about 10 days later, which tested general data science techniques. I then spoke to the hiring manager two weeks later for an hour about some details about the role, which did not sound as expected. A data challenge was then issued on a generic Kaggle-esque task. I came in for an in person interview 5 weeks later, comprising more standard data science questions (basically word problems with linear equations and some faux-consulting role play involving decision trees and ROC curves). I did not receive feedback for two weeks following the interview. At this point, the only part of the interview that touched on the skills involved in my role was the casual call with the hiring manage. After a three month process involving no test of my capability to perform the tasks involved in the role, I was rejected for not having the experience necessary for the position. Without disclosing identifiable information, despite being in the field a relatively short time, I have published in top machine learning venues and developed, by a wide margin, a state of the art technique within the field for which I had applied. The idea that my experience had been determined in an interview process that involved no technical assessment of the capabilities for the position at hand is the definition of insanity. I would like to praise the positivity provided by the recruiter throughout the process; that said, Capital One desperately needs to shorten its process, ask relevant questions, and get their heads out of the sand if they actually want to attract talent. Don't waste applicants' time like this, and don't vet using irrelevant metrics and capricious intuition.

      Interview questions [2]

      Question 1

      how would you build a churn model
      Answer question

      Question 2

      how would you do topic modeling on tweets
      Answer question
      The process took 2 weeks. 

      Interview

      First round is the Hiring manager call Where talked about experience and matching skill set Then online assessment- but the tool is outdated and hard to use Timed and protocols

      Interview questions [1]

      Question 1

      Your data science experience and previous projects
      Answer question

      Principal Data Scientist Interview

      30 Oct 2024
      Anonymous employee
      Accepted offer
      Positive experience
      Difficult interview

      Application

      I applied through an employee referral. I interviewed at Capital One in Oct 2024

      Interview

      First, there’s a 30-minute call with the hiring manager, focusing on background, experience, and role fit, with no technical questions. Next is a take-home assessment, to be completed within 7 days, where you’ll build an end-to-end data science solution from data loading to model creation and evaluation. Successfully completing this makes you eligible for the final interview stage, called 'Power Day.' Power Day consists of four 1-hour interviews: Case Study: Includes questions like assessing if a car company should launch a new product. Check out the 'Hacking the Case Interview' YouTube channel for tips. Roleplay: You’ll receive a presentation (PDF/PPT) to study for 10 mins and explain and discuss with the interviewer about what insights you can establish from that. Technical: A live coding interview where Python and shell scripting skills are tested. Behavioral: Covers job-fit questions and situational questions like 'Tell me about a time when…

      Interview questions [1]

      Question 1

      1. Tell me about a time when the project didn't went as expected
      Answer question
      3

      Principal Data Scientist Interview

      8 Aug 2024
      Anonymous interview candidate
      No offer
      Neutral experience
      Average interview

      Application

      I applied online. I interviewed at Capital One in Aug 2024

      Interview

      I interviewed for the Principal Data Scientist role at Capital One. I only made it through the 30-minute Technical Interview round; I did not advance to the take-home project or all-day interview. I wrote down every question I was asked - it was mostly just vague machine learning/statistics questions.

      Interview questions [12]

      Question 1

      "Describe a few different methods of sampling."
      Answer question

      Question 2

      "How would you address imbalanced data?"
      Answer question

      Question 3

      "How can you ensure that a model does not have multi-collinearity?"
      Answer question

      Question 4

      "Describe the difference between linear and logistic regression."
      Answer question

      Question 5

      "How would you design an A/B test to determine whether a campaign is working?"
      Answer question

      Question 6

      "What criteria would you use to determine how reliable the control and test group are?" (follow-up to question #5)
      Answer question

      Question 7

      "What are ways to increase the speed and efficiency of a model?"
      Answer question

      Question 8

      "What are examples of hypertuning methods?"
      Answer question

      Question 9

      "What would you do if there is a concern that your model is overfitting?"
      Answer question

      Question 10

      "You are using a performance matrix to see how well a model performs. Describe the matrix."
      Answer question

      Question 11

      "What is the difference between bagging and boosting?"
      Answer question

      Question 12

      "How would you identify outliers?"
      Answer question
      3