One 90 minutes technical screening with questions on statistics, applied deep learning, machine learning theory and a easy coding question on TF-IDF Got opportunity to do onsite interview. 5 rounds total Round 1: ML depth, the interviewer simply took the problem he worked on in zalando, forecasting and marketing intelligence. I.e. modelling the bidding of Google keywords during search for placing their ads to users. I had no experience with reinforcement learning, he expected multi arm bandit solution. Round2: Research chat with Director of Casual inference team. This was chat with director of different team to see scientific thinking. Overall went well imo. Round3: Coding round, had a dynamic programming / recursion question where i had to break strings in given set into smaller strings based on reference set. Eg: if reference set has {pan, cake} then break pancake in main set to {pancake : [pan, cake]} Was able to code the solution and interviewer was quite happy with solution. Round 4: Hiring manager round, it had mostly behavioural questions judging team skills in techincal scenarios. Also questions on how do I structure, test and review my code. Round 5: HR round, non technical behavioural round. Went well. Overall feedback: Coding excellent, ML stat ok however this is senior position and needs more experience. Very diplomatic feedback from the process that i don't appreciate.
Applied Scientist Interview Questions
1,182 applied scientist interview questions shared by candidates
The hiring manager and the recruiter basically ghosted me, so no questions were asked.
- Talk to me about your PhD project, describe a technical problem you had and how you solved it - You are asked to quality control the 3D size of an item, how would you do it?
First interview about big data and OLS
Tell me about yourself and relevant projects for this role
The usual recruiter and managerial interviews questions about ones background. Some ML/optimization questions (not entirely related to the job description) in the deep dive, and for the coding interview, there was floodfill problem. Rather easy.
SQL related scenario based question. Basic reporting questions. Python questions.
- Tell me why you're interested in this domain. - Tell me about relevant projects you've done in this domain. - What implications would a multi-modal generative AI tool have (compared to a unimodal one) and how would you go about mitigating its biases. - Case Study: A company wants to use an AI tool to summarize managers' feedback for employees. How would you "systematically" approach identifying the biases of such a tool?
To describe how to use transactional data to promote online sales
Explain Machine Learning algorithms like Kmeans clustering , Linear regression and algos specific to predictive modelling . How to choose no of clusters in K means
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