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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.
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Applied Scientist

Interviewed at Zalando

3.6★
Apr 19, 2022

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.

 Case studies 1. You client is retailer store owner like DMart (offline online store) , the store's sales for softdrinks have done down 15% from last quarter. What can be possible reasons for this and steps to tackle each cause. 2. You are running amazon like platform. How will you choose your Top100 'gold' customers to whom you will give rewards like discounts and coupons etc.
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Applied Data Scientist

Interviewed at dunnhumby

4.3★
Jan 3, 2022

 Case studies 1. You client is retailer store owner like DMart (offline online store) , the store's sales for softdrinks have done down 15% from last quarter. What can be possible reasons for this and steps to tackle each cause. 2. You are running amazon like platform. How will you choose your Top100 'gold' customers to whom you will give rewards like discounts and coupons etc.

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