Senior Data Scientist applicants have rated the interview process at Datadog with 3.5 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 100% positive. To compare, the company-average is 48.8% positive. This is according to Glassdoor user ratings.
Candidates applying for Senior Data Scientist roles take an average of 14 days to get hired, when considering 2 user submitted interviews for this role. To compare, the hiring process at Datadog overall takes an average of 26 days.
Common stages of the interview process at Datadog as a Senior Data Scientist according to 2 Glassdoor interviews include:
One on one interview: 33%
Skills test: 33%
Phone interview: 33%
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The interview process was very well organized and professional. Elisa Medina, the recruiter, was extremely friendly, supportive, and communicative throughout the process. I especially appreciated her transparency, kindness, and effort to coordinate the interviews. The conversations with the team were thoughtful and relevant to my experience as a Senior Data Scientist. Overall, I had a very positive candidate experience.
I applied through a recruiter. I interviewed at Datadog (Paris) in Feb 2026
Interview
HR call
Technical Screening (includes both fundamental ML / statistics and applied case study around time series anomaly detection).
Full loop interview including case study, data analysis case, ML architecture design, coding, and behavioural / leadership interview.
Interview questions [1]
Question 1
Explain how to run a linear regression on a very large dataset.
The closed form solution cannot be considered.
You should look at approximations.
I applied through an employee referral. The process took 2 weeks. I interviewed at Datadog in Nov 2025
Interview
Had a phone screening call with Recruiter covering past experience and current aspirations. Passed on to the Technical Fundamentals interview that focused anomaly detection on a time series and OLS regression with computational constraints
Interview questions [1]
Question 1
You are given an extremely large dataset with a target column. How would you go about modeling it assuming the dataset is too big to processed in memory?