Credit Karma Data Scientist Interview Questions
& Process
Real candidates share what happened, how many rounds they had,
and how the experience turned out.
Based on 9 interview experiences · FREE TO READ
Candidate interview experiences
First-hand accounts from people who interviewed at Credit Karma.
Credit Karma
Data Scientist
Senior Average Negative experience No offer 5 rounds
Interview process
Recruiter call Phone screen Technical screen Onsite Offer
Interview formats
Coding Technical System Design
The process kicked off with a recruiter chat, followed by the hiring manager. Then there was a LeetCode coding round focusing on basic ML concepts, and finally, an onsite technical part. The onsite had two separate rounds: one on Recommendation Systems and another covering General ML, Recommendation Systems, and System Design. On the plus side, the interviewers were really respectful and listened well. But, communication was a big issue; it was never clear what was happening next or what to study. I had to chase them for info about the next steps and scheduling, which caused a lot of delays and rescheduling. It was also confusing with so many HR people on emails. After about three months and five rounds, I got an automated rejection, which was pretty frustrating. Also, during one of the virtual onsite interviews, the interviewer was on call and kept getting distracted by Slack messages, which made it hard to focus.
Confirmed questions7 questions
- Tell me about Dynamic Programming.
- Explain the difference between kNN and ANN, including why and when to use each.
- Describe your depth and breadth of knowledge in recommendation systems.
Credit Karma
Data Scientist
Senior Difficult Positive experience No offer 4 rounds
Interview process
Recruiter call Technical screen Technical screen Technical screen
Interview formats
Coding Technical
CK reached out on LinkedIn. They were responsive and aware that I was interviewing elsewhere and might get an offer soon, so they sped things up for me - the whole thing took about a week. It was a good experience overall, even though it didn't work out for a few reasons. They also gave me really detailed feedback afterward. In my experience, how a company treats you during interviews is how they'll treat you when you work there.
Confirmed questions4 questions
- Was there an HR screening?
- A chance to meet the team.
- A coding interview, specifically a pair programming exercise.
Credit Karma
Data Scientist
Entry Average Neutral experience No offer 2 rounds
Interview process
Recruiter call Technical screen Phone screen
Interview formats
Behavioral Technical Coding
Applied online and got a recommendation from an alum. Got an email for an initial HR chat, which was good. Just basic stuff about me, my schooling, my projects, and what I'm looking for in a first job. About a week later, I had a technical interview with a Machine Learning engineer. He asked some general Deep Learning theory questions, not super specific, and it was a bit unclear how to answer. Seemed like he memorized some concepts but couldn't really explain the questions well. Also had a coding question on an online platform. The question itself was easy, but the site had issues with tabs and shortcuts.
Confirmed questions1 question
- In your view, what distinguishes the roles of a Data Scientist and a Machine Learning Engineer?
Credit Karma Data Scientist Interview Questions
Quoted word for word from Credit Karma interview reports.
“Explain the difference between kNN and ANN, including why and when to use each.”
Read reports →“How do you construct the loss/cost function when training recommendation models?”
Read reports →“What are the benefits of different embeddings, and when and why should each be used?”
Read reports →“Could you explain a machine learning model?”
Read reports →“In your view, what distinguishes the roles of a Data Scientist and a Machine Learning Engineer?”
Read report →“Can you do a case study on binary classification?”
Read report →“Tell me about Dynamic Programming.”
Read report →“Explain how recommendation system knowledge ties to business metrics and needs, including when, where, and how.”
Read report →“What about a recommendation system case study?”
Read report →Formats, difficulty and experience
Across all 9 Credit Karma interview reports.