Two candidates describe Nokia Data Scientist interviews that mix Python coding, machine learning fundamentals, and project discussion. One also reports a large take-home assignment. Both ended without an offer.
$124K
Avg. Base Comp
$150K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Nokia's Data Scientist interview, as two candidates describe it, is practical rather than puzzle-driven. Both had a short HR conversation early on and then moved into technical discussion, but the middle of the process differed. Evidence is limited to two reports, so treat the details as examples rather than a fixed format.
One candidate, interviewing for a Full Stack Data Scientist opening, had a technical round with a business representative and a data scientist. It included a simple Python question about returning the intersection of two lists, then questions on overfitting and imbalanced datasets. The interviewers wanted clear, grounded answers rather than buzzwords. This candidate was also given a large take-home assignment on database construction and analytics, which took far more effort than expected. They were rejected about two weeks later with no explanation.
The other candidate reports an online screen on Python fundamentals and basic statistics, then a 30-minute HR call. Next came a three-person panel that asked for a detailed walkthrough of a past project, followed by questions on gradient boosting, XGBoost, PCA and feature correlation. A second technical round covered coding and machine learning case studies, with emphasis on time complexity and optimization. The final conversation with the hiring manager covered culture fit, team dynamics and timeline expectations. This candidate did not receive an offer.
To prepare, be ready to explain why you chose each modeling and feature-engineering step in your own projects. Practice explaining how you handle class imbalance and overfitting out loud. Rehearse standard Python problems while stating their complexity. If a take-home arrives, budget real time to build a database and analyze it end to end.
Synthesized from 2 candidate reports by our editorial team.
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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 | |
| Find Duplicate Numbers in a List | |
| Swapping Nodes | |
| Hurdles In Data Projects | |
| Success Measurement | |
| Testing Price Increase | |
| Swap Variables | |
| Data Preparation for Imbalanced Data | |
| Overfit Avoidance | |
| String Palindromes | |
| Impossibly Iterative Fibonacci | |
| Check Matching Parentheses | |
| Netflix Price | |
| Your Strengths and Weaknesses | |
| Singly Linked List | |
| Delivery Fees | |
| 2nd Highest Salary | |
| Merge Sorted Lists | |
| Over-Budget Projects | |
| Closed Accounts | |
| Bagging vs Boosting | |
| Cyclic Detection | |
| Size of Joins | |
| Sort Strings | |
| Get Top N Frequent Words | |
| CNNs vs Intensity-Based Features | |
| Random Forest Explanation |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an early screening step. One describes a quick HR call about availability and salary expectations, with the technical interview scheduled for the next day. The other describes an online Python and basic statistics test followed by a 30-minute HR call on background and interest.
One candidate met a business representative and a data scientist and got a simple Python list-intersection question, then overfitting and imbalanced-data questions. The other faced a three-person panel and reports a detailed project walkthrough plus questions on gradient boosting, XGBoost, PCA and feature correlation.
The process may include applied work, but candidates describe it differently. One reports a second technical round with standard coding problems, ML case studies and a focus on time complexity. The other reports a very large take-home on database construction and data analytics that took far more effort than expected.
One candidate reports a final hiring manager conversation about culture fit, team dynamics, timeline and growth expectations. The other reports a rejection about two weeks after the assignment with no explanation. Both outcomes were negative, so plan for the possibility of little feedback.
