Product Sense Question - How do you tag an incoming unknown product with amazon catalogs (such as toys, books, electronics). Provided description and a couple of pics about of the product Programming Question - Iterating through a list of tuples and performing some algorithm on the tuple values (simple one).
PCA and similar methods, signal processing
First round: - phone/video interview with one person. - Asked the typical DS interview questions (overfitting/cross validation), talked about my ML experiences. - Asked a basic coding question: given an animal, print out the noise it makes. Basic things like polymorphism/inheritance, briefly touched on string similarity Second round in person (5 interviews): - Lots of leadership principle/behavioral questions - One coding interview. Given a database of book titles and number of copies sold, how do you identify the top N most-sold books. Basic algorithm/data structures of things like priority queues/heaps, space-time complexity analysis, live-coding. Even if you miss the correct data structure, they provide some hints along the way so you can complete the problem - Multiple DS interviews, from things like typical DS interview questions and your ML experience, to an applied DS question (deduplicating transactions, how would you solve this problem, how would you build/train/score a model, how would you scale it)
How did the last product you worked on help the customer?
Write down the pseudo-code of Kmeans Detailed questions of random forest methods
1. How to handle Underfitting? 2. Bagging vs Boosting 3. Performance metrics: Precision, Recall, AUROC 4. Word vectors (NLP)
[Online coding question] TwoSum. Return the indice of two numbers that sum up to a given target number.
[ML/DL] Explain Language Modeling
a lot of behavior questions. also some very basic machine learning and algebra questions.
1st onsite interview: Technical/Research questions included: YOLO and its derivatives, SVMs and kernels, Metric learning, Segmentation (semantic vs instance), non-dl image matching (i.e SIFT features), clustering methods, supervised vs non-supervised learning etc.
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