I applied through a recruiter. The process took 2 weeks. I interviewed at SoftServe (Warsaw, Masovia) in Jun 2025
Interview
The interview consisted of multiple rounds covering both technical and practical aspects of data science work. They asked about my previous work experience, tools I used, data processing scale, coding practices, and MLOps skills. The session included classic machine learning questions about when to use traditional ML versus deep neural networks, and detailed questions about ensemble methods, boosting, bagging, XGBoost, random forests, and decision trees. They also inquired about challenges I faced in previous projects and how I resolved them. After interview I got my interview feedback in the next day.
Interview questions [1]
Question 1
They asked comprehensive questions about my practical data science experience: What did you do in your previous job? What tools did you use? Did you process large datasets? Do you write code and how do you ensure clean code practices? What are your MLOps capabilities? What problems did your solutions have in your previous work and how did you fix them? They also asked classic ML questions: When is classical machine learning better than deep neural networks? What is boosting, bagging, XGBoost, random forest, and decision trees in general?
I applied online. The process took 3 weeks. I interviewed at SoftServe in Nov 2023
Interview
There were three stages - one screen HR call, then one technical interview, then one management call. The HR call was about the expectations - both about the company and salary. The technical interview was all about past data science experience (+ some additional theoretical questions, but they weren't hard).
I applied online. The process took 2 weeks. I interviewed at SoftServe in Aug 2023
Interview
Technical questions about particular topics in ML. Focused on Time series and NLP. However questions related to methods on how to evaluate machine learning models were also asked. Experience with GPU training and pytorch knowledge will help a lot.
Interview questions [1]
Question 1
Clustering on time series
Explaining k-fold cross validation
Fine tuning BERT
