Questions d'Entretien pour Data Scientist 2026

Transforme les données en modèles et décisions business quantifiables.

Questions fréquentes

1. 1. You receive an imbalanced dataset to predict churn. What steps do you take before choosing the model?

   Guide de réponse: Talk about exploration (positive-class distribution), temporal vs random split, balancing techniques (resampling, class_weight) and the right metric (recall, AUC) instead of accuracy.

2. 2. Describe the last tough code review you received. How did you process it and what changed in how you write code?

   Guide de réponse: Show openness to feedback. Cite the specific observation, what you refactored and the habit you adopted to avoid the pattern going forward.

3. 3. Tell me about a critical production bug that you fixed. How did you isolate it, what tools did you use and what came out of the post-mortem?

   Guide de réponse: Walk through detection (alerts, logs, metrics), the first hypothesis you validated and the safeguard you added so it cannot recur silently.

4. 4. When you have to choose between shipping speed and technical quality, how do you balance it?

   Guide de réponse: Avoid the political answer. Give a concrete example with explicit trade-offs: business deadline, accumulated tech debt, risk of the change and why you yielded.

5. 5. Why are you interested in this role specifically and not another at similar seniority?

   Guide de réponse: Connect three dots: what the company does, the technical problem that excites you and the skill you want to deepen over the next 12-18 months.