HR interview, then call with teamleader, then case study and interview with more team members. Professional approach, respectful towards the candidates, I felt that the company provides me with all the necessary information
Other Data Scientist interview reviews for T-Mobile
I applied online. I interviewed at T-Mobile (Prague) in Apr 2025
Interview
The first round was an online interview where we talked about general things and a bit about my experience - very straightforward. After passing that round, I received a take-home assignment, with less than a week to complete it (in my case, it involved one of the Kaggle datasets and several related questions). The task was to analyze the dataset and create a presentation - mostly using a Jupyter notebook for visualization, and also applying some NLP techniques like clustering. Then, the second stage was to present this work in person. After my presentation, I was told that I would be contacted at the beginning of the following week once all other presentations had been completed, and that I would receive feedback regardless of the outcome.
A week passed and no one got in touch. Another week later, I reached out to the recruiter to ask what happened - she contacted the team, and I was eventually told, 'Everything was good, but we chose another candidate.'
I’m totally fine with not being selected, I was mentally prepared for that. What I don’t understand is the unprofessional communication. I spent significant time on a challenging take-home assignment, prepared and delivered a full presentation in your office, and you couldn't even provide proper feedback as promised?
I applied through an employee referral. The process took 1 week. I interviewed at T-Mobile (Atlanta, GA)
Interview
1. Initial Screening: Assessing candidates' qualifications and experience through resume review and an initial phone or video interview to gauge their understanding of key data science concepts and skills.
2. Technical Assessment: Evaluating candidates' technical proficiency with data manipulation, analysis, and machine learning through a coding challenge or case study, assessing their problem-solving abilities.
3. Final Interview: Conducting a comprehensive interview with a panel, focusing on both technical and soft skills, to assess communication, collaboration, and domain knowledge while ensuring alignment with the organization's values and goals.
Interview questions [1]
Question 1
1. **Can you discuss a complex data science project you've led, highlighting key methodologies and outcomes?**
2. **How do you handle missing data in a dataset, and what impact might it have on model performance?**
3. **Explain a machine learning algorithm to a non-technical stakeholder, emphasizing its business implications.**
4. **Why is regularization important in machine learning, and how does it affect model generalization?**
5. **Describe your approach to staying updated with the latest trends and advancements in the field of data science.**