I just recently helped a classmate review his work in the Spotify Spotify's Data Scientist interview, the whole experience is quite fresh, so to give you a detailed pick. The overall feeling is that Spotify's interview process is a bit friendlier than that of many large North American companies, and the interviewer's attitude is very good, and the questions aren't too tricky, but the requirements for details are still quite high, especially for the resume part, which is not to be fooled at all.
First round: Hiring Manager interview
The interview started with a resume session. hm opened up the student's project experience and threw in a question in the first sentence:
"How much user behavior data did you have at the time? How was it stored and cleaned?"
The classmate froze and immediately switched to the details of the project:
- The data source comes from online logs, several hundred million events per day;
- It is stored in the Hive table, with sampling and partitioning done upfront;
- The main problem encountered during the cleaning process was that the user ID was missing and the session needed to be filled in.
HM followed up immediately:
"How do you guys determine that the division of sessions makes sense? Have you tried different thresholds?"
This question is very "Spotify style", favoring business sense, but fortunately, my classmates had prepared in advance and answered that they had tried 30 minutes and 1 hour thresholds, and finally chose by comparing retention and click-through rate.
This round feels especially like "reviewing a real project", not just going through the motions, but asking you to explain why you did what you did, HM will nod for a while after listening, and then continue to ask about the impact, until the whole pipeline is finished. It feels like you are giving a report to a superior who understands the business very well, so there is no room for ambiguity.
Round 2: Technical Interview (SQL + Python + Stats)
The atmosphere of this round of technical interview was more relaxed than expected, the interviewer first exchanged pleasantries and said with a smile, "Don't be nervous, I'll give you hints if needed." The atmosphere immediately relaxed a lot.
SQL section
The title is very common:
"Given a table of purchases, find the date of each user's most recent purchase and sort by date."
Classmates wrote on the spot:
SELECT user_id, MAX(purchase_date) AS last_purchase
FROM purchases
GROUP BY user_id
ORDER BY last_purchase DESC.
The interviewer looked and nodded and added a follow-up:
"What if I still want to bring out the purchase amount as well?"
This brings us to the window function. The classmate immediately changes it to:
SELECT user_id, purchase_date, amount
FROM (
SELECT user_id, purchase_date, amount, ROW_NUMBER(
ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY purchase_date DESC) AS rn
FROM purchases
) t
WHERE rn = 1
ORDER BY purchase_date DESC.
Ran through it on the spot and the interviewer smiled and said "Perfect". The whole SQL section had a relaxed atmosphere, but you could sense that Spotify wanted to make sure you were really familiar with window functions and not just basic group by.
Python section
The second question is the classic anagram:
"Write a function to determine whether two strings are mutually rearranged."
The student wrote the most straightforward solution directly:
def is_anagram(s, t).
return sorted(s) == sorted(t)
The interviewer pursued the question:
"What is the time complexity of this method? Can it be faster?"
Classmate Answer:O(n log n), and then added, "It can be counted in a dictionary, reducing the complexity to O(n)."
So the optimized version was written again:
from collections import Counter
def is_anagram(s, t): return Counter(s) == Counter(t).
return Counter(s) == Counter(t).
The interviewer immediately nodded, "Great, that's exactly what I was expecting."
You can feel Spotify's style here: first let you write what you can run, then push you towards a better solution. You won't be pushed to write the optimal solution right off the bat.
Statistics section
The last question was replaced with a statistic:
The interviewer displayed a regression table and asked:
"Can you explain what the coefficient here means?"
There's one in the table. coefficient = 2.3, the corresponding variable is "time spent on app".
Classmate Answer:
"Holding all other variables constant, an additional minute spent by a user on the app increases target metrics (e.g., ad clicks) by an average of 2.3 units."
The interviewer pursued the question:
"What do you think if the p-value of this variable = 0.6?"
A classmate immediately added:
"This means that the probability that we observe the current result is high provided that the null hypothesis holds, so this coefficient is not significant enough to show that time spent has a real effect on target."
The interviewer smiled and said "Good point" and then nodded into a summary.
Spotify DS Review and Preparation Suggestions
The Spotify DS interview is not a question-and-answer type of interview, but emphasizes more on explanation, expression, and business understanding. The questions are not too difficult, but the questions are very detailed, especially in the HM interview round, almost asking you to tell every detail of your resume to the bone.
Suggested preparation:
get through with a resume: Don't write about something you haven't done yourself. Every project should be able to talk about data sources, processing details, modeling choices, and business results.
SQL: Practicing through window functions, sorting, aggregation is enough for most scenarios.
Python: Familiarity with LeetCode high-frequency easy/medium, especially string processing and hash tables.
statisticians: Have the p-value, hypothesis testing, and regression coefficient interpretations ready and able to speak in simple terms in relation to the scenario.
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