ML questions, Computer Vision Questions, Coding focus on Data Structures with thoughts on dynamic programming.
Applied Scientist Interview Questions
1,167 applied scientist interview questions shared by candidates
- What is the motivation to apply for this position? - What is the most difficult point in your research?
Each interviewer asked 2 LP questions.
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)
Basic probability/statistics questions, simulation and coding
ML round - asked questions about overfitting, model design, precision, recall, F1 scores Coding round - design algorithm to implement byte pair encoding
Major questions were related to my project which was computer vision/DL based. 1. What are the difficulties I faced in my project and how I counteracted it? 2. Explain U-Net and what novel thing it offers? 3. Explain regularization and it's different types? 4. How KL divergence loss is different from cross entropy loss?
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