Dataiku Data Scientist Interview Questions
& Process
Real candidates share what happened, how many rounds they had,
and how the experience turned out.
Based on 26 interview experiences · FREE TO READ
Candidate interview experiences
First-hand accounts from people who interviewed at Dataiku.
Dataiku
Data Scientist
Mid Difficult Neutral experience No offer 4 rounds
Interview process
Recruiter call Presentation Technical screen Onsite Panel Group Presentation Onsite
Interview formats
Presentation Technical Behavioral Case
We started with a 10-minute presentation where I had to explain how I solved a business problem using a technical solution, followed by a 35-minute technical interview. Then there was a 90-minute roleplay where two data scientists acted as stakeholders (one technical, one non-technical) and I had to scope out the problem. Finally, I had a 45-minute interview with the hiring manager.
Confirmed questions1 question
- Can you explain what a decision tree is in a way a 5-year-old would understand?
Dataiku
Data Scientist
Senior Easy Positive experience No offer 1 round
Interview process
Recruiter call
Interview formats
Behavioral
I had a first interview that wasn't technical and it felt really constructive and genuine. The interviewer had a positive attitude. I didn't get the job in the end, but the interviewer was upfront about why I wasn't chosen (they needed someone more senior with stakeholder experience) and was happy for me to apply for other roles that might be a better fit.
Confirmed questions1 question
- What are you seeking in your next position?
Dataiku
Data Scientist
Senior Average Neutral experience No offer 3 rounds
Interview process
Recruiter call Technical screen Onsite
Interview formats
Behavioral Technical Case Presentation
Had a screening interview with HR. Then an interview with the hiring manager where we discussed a past project. After that, a technical screening with two Data Scientists. They presented an ML use case simulating a client scoping session: prediction of gas consumption. They walked me through all the ML steps: Data understanding, Handling missing features, Model selection, KPIs. I was given time to ask questions and make sure I understood the use case before starting.
Confirmed questions4 questions
- What is the difference between MSE and MAE, and when would you use each?
- Why is it necessary to normalize features for linear regression?
- Can you describe a past project?
Dataiku Data Scientist Interview Questions
Quoted word for word from Dataiku interview reports.
“What's the difference between K8s and Docker?”
Read reports →“How would you estimate the total number of buses in London daily?”
Read reports →“Why is it necessary to normalize features for linear regression?”
Read reports →“What is the difference between MSE and MAE, and when would you use each?”
Read reports →“Suppose a travel agency website recommends destinations using textual and price information, plus photos. How can we leverage the destination photos to improve the recommendation system?”
Read report →“If you were to be reincarnated, what animal would you choose to be?”
Read report →“Could you explain what a random forest is?”
Read report →“Can you explain what a decision tree is in a way a 5-year-old would understand?”
Read report →“How would you approach the task of predicting US population income classification?”
Read report →Formats, difficulty and experience
Across all 26 Dataiku interview reports.