One coding round followed by two technical rounds.
The coding round involved questions on data manipulation using SQL and Python.
The technical rounds included discussions on past experience and ML depth and breadth. For example.
How does the XGBoost classifier work, step by step.
SQL:
Python:
How does XGBoost work?
How to represent categorical columns with high cardinality?
How can embeddings be generated?
How might an encoder-decoder help in this?
How to find the presence of multicollinearity in data?
What is the chi-square test of independence?
How to handle imbalanced classification?
Why is PR AUC better suited to such cases than ROC AUC?
How to handle a large number of classes in multiclass classification?
What is negative sampling and how does it help in such scenarios?
The following metrics were computed from 1 interview experience for the PayPal Machine Learning Specialist role in India.
PayPal's interview process for their Machine Learning Specialist roles in India is extremely selective, failing the vast majority of engineers.
Candidates reported having very good feelings for PayPal's Machine Learning Specialist interview process in India.