Nokia Data Scientist interview typically runs 3 rounds: HR phone screen, technical interview, take-home assignment. It usually takes about 2 weeks and is unusually heavy because of the large assignment.
$140K
Avg. Base Comp
$147K
Avg. Total Comp
3
Typical Rounds
2-3 weeks
Process Length
Our candidates report that Nokia cares less about polished theory and more about whether you can reason cleanly from first principles. In the live conversation, the questions stayed grounded: a basic Python task, then practical discussion around overfitting and imbalanced data. That pattern tells us the bar is not about impressing with jargon; it’s about giving direct, technically sound answers that a business stakeholder and a data scientist can both trust.
The bigger signal is the assignment. Multiple candidates describe it as unusually demanding for the process, with a strong emphasis on database construction and analytics. That suggests Nokia is screening for people who can handle messy, real-world data work end to end, not just model selection in isolation. We’ve seen that the work can feel broad rather than tricky, which means the evaluation likely centers on whether your approach is structured, complete, and defensible.
A recurring theme is that the process can feel smooth early on and then become heavy in the take-home phase. That combination usually rewards candidates who are comfortable with ambiguity and can produce something that looks like usable work, not a classroom exercise. In our view, Nokia is testing for practical judgment under scope: can you make sensible tradeoffs, explain them clearly, and build something that would hold up in a connected-world product environment?
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Nokia process.
The first stage was a quick phone call with HR, mostly to go over my availability and salary expectations. They seemed to be in a rush and actually scheduled the technical interview for the next day. That second round was with a business representative and one data scientist, and it felt more practical than theoretical. I was asked a simple Python coding question about returning the intersection of two lists, and then the conversation moved into core data science topics like how to deal with overfitting and imbalanced datasets. The technical part was not especially hard in terms of algorithms, but it was broad and they wanted clear, grounded answers rather than buzzwords.
What stood out most was the assignment. It was described as very demanding and was centered on database construction and data analytics. I wouldn’t call it difficult in the sense of tricky logic, but it was a very large task for an interview process, and it took much more effort than I expected. The first interview and the assignment both seemed to go well, and the team appeared satisfied during the process, but I was rejected about two weeks later without any explanation. Overall, I’d say the process was straightforward at the start and then became unusually heavy because of the size of the take-home work.
Prep tip from this candidate
Be ready for a fast-moving process: the technical round included a basic Python list-intersection problem plus standard data science questions on overfitting and class imbalance. Also expect a large take-home assignment focused on database construction and analytics, so practice working through a substantial end-to-end task efficiently.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Nokia
Given an integer N, write a function that returns all of the prime numbers up to N
| Question | |
|---|---|
| The Brackets Problem | |
| Append Frequency | |
| Groups of Anagrams | |
| Hurdles In Data Projects | |
| Find Duplicate Numbers in a List | |
| Success Measurement | |
| Swapping Nodes | |
| Testing Price Increase | |
| Swap Variables | |
| Data Preparation for Imbalanced Data | |
| Overfit Avoidance | |
| String Palindromes | |
| Impossibly Iterative Fibonacci | |
| Check Matching Parentheses | |
| Your Strengths and Weaknesses | |
| Netflix Price | |
| Singly Linked List | |
| Delivery Fees | |
| 2nd Highest Salary | |
| Merge Sorted Lists | |
| Over-Budget Projects | |
| Closed Accounts | |
| Bagging vs Boosting | |
| Size of Joins | |
| Sort Strings | |
| Get Top N Frequent Words | |
| Cyclic Detection | |
| Random Forest Explanation | |
| Longest Increasing Subsequence |
Synthesized from candidate reports. Individual experiences may vary.
A quick call with HR focused on availability, salary expectations, and basic fit. In this case, the recruiter moved quickly and scheduled the next interview for the following day.
A practical interview with a business representative and a data scientist. It included a simple Python coding question, plus broader data science topics such as overfitting and handling imbalanced datasets.
A demanding assignment centered on database construction and data analytics. The work was described as large in scope and required significantly more effort than expected for an interview process.
After the assignment and earlier interviews, the candidate heard back roughly two weeks later with a rejection and no detailed explanation.