all ML questions, python and sql
Sr Data Scientist Interview Questions
3,509 sr data scientist interview questions shared by candidates
Qu'est-ce que vous referiez différement dans un poste précédent ? Mon client n'a pas confiance dans le modèle de Machine Learning proposé, que faire ?
Stats: 1. Fundamental laws 1.1. Explain Central Limit Theorem (CLT)? 1.2. Explain Law of Large Number (LLN)? 1.3. What are their differences? How are they beneficial? 2. Statistical Tests 2.1. Tell me the differences/conditions between T-Test vs Z-Test are? When is each of them used? 2.2. When is t-distribution used as opposed to normal distribution? 2.3. How many data points are considered good enough to use each of them? 2.4. How does each distribution look like? (skewness and kurtosis viewpoint) 2.5. Explain p-value in a layman language with a simple example. 2.6. If we run the t-test multiple times, what will happen to the strength of the statistical test? (Bonferroni Correction) 2.7. When is the Chi-Squared test used? How does the distribution look like?
ML: 1. Linear Regression: 1.1. Explain L1 vs L2? 1.2. How does each affect the coefficients? 1.3. Explain assumptions of linear regression. 1.4. How is each assumption tested? 1.5. If each assumption is violated, what are their remedies? 2. PCA 2.1. Explain PCA. 2.2. Walk me through the algorithm step by step. 2.3. How is the formula constructed? 2.4. What is the relationship between PC1 and PC2? 2.5. How is orthogonality preserved in the mapped feature space? 2.6. How do you run the feature importance in PC-mapped feature space? 3. ML Algorithm 3.1. Explain the ensembling method. 3.2. Explain the differences between XGBoost and Random Forest? 3.3. When is each used? Pros and cons? 3.4. Which one is computationally expensive and why? 3.5. What are the feature selection methodologies? 3.6. Imagine we have a multivariate KPI that most of the features are correlated. Now we are noticing a spike in the KPI, how do you determine which feature has the highest effect on it? (Feature importance analysis for Temporal shock)
KPIs de glovo, por qué Glovo
Tipical HR interview, what do you know about Glovo etc etc
- questions techniques en Python (il y a un test à la maison à faire le week-end) - demandent le nom des autres boites auxquelles on candidate
SQL, machine learning related questions
Focus on my projects, AWS knowledge, data science and ML basics
One of the questions at the panel interview focused on how to handle high-cardinality categorical variables in a model, especially when a naive approach could introduce noise or performance issues. The discussion was very practical and centered on trade-offs rather than theoretical tricks.
Viewing 2861 - 2870 interview questions
See Interview Questions for Similar Jobs
Jr Data ScientistData Scientist TraineeData Science EngineerSenior Machine Learning ScientistApplied Research ScientistSenior Data ScientistMachine Learning Research EngineerData Scientist NlpMachine Learning Research ScientistSenior Machine Learning EngineerData SciencesMachine Learning ScientistSoftware Engineer Machine Learning ScientistNlp Data ScientistResearch Scientist Computer VisionApplied ScientistSr. Data ScientistSenior Quantitative Researcher