General Data Scientist interview prep
6 live roles at GoCardlessMachine learning, statistics, Python and case studies — the data science interview for analytics-heavy product and consulting teams.
Typical Data Scientist interview rounds
- 1 Statistics & probability. Hypothesis testing, distributions and inference.
- 2 Machine learning. Algorithms, bias-variance, overfitting and evaluation metrics.
- 3 Coding (Python/SQL). Pandas, NumPy and SQL data manipulation.
- 4 Case study / ML design. Frame a business problem as an ML problem end to end.
Commonly-asked Data Scientist questions
- Explain the bias-variance tradeoff.
- What is overfitting and how do you prevent it?
- Explain the difference between supervised and unsupervised learning.
- How does a random forest work, and why is it better than a single decision tree?
- What is the difference between precision and recall, and when do you optimise for each?
- Explain how logistic regression works and what the sigmoid function does.
- What is regularisation (L1 vs L2) and why do we use it?
- How do you handle imbalanced datasets?