J'ai postulé en ligne. J'ai passé un entretien chez T-Mobile (Prague) en avr. 2025
Entretien
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?
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
J'ai postulé via la recommandation d'un employé. Le processus a pris 1 semaine. J'ai passé un entretien chez T-Mobile (Atlanta, GA)
Entretien
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.
Questions d'entretien [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.**