Describe a time when your colleague has confronted you about work. Which is the best technical project you have worked upon?
Associate Scientist Ii Interview Questions
700 associate scientist ii interview questions shared by candidates
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)
On the phone screen, I was asked questions typical of the area of applied science I applied. Since the phone screening was easy, I did not prepare a lot for the onsite. However, at the on-site, they asked me way deeper questions than I expected and I failed to answer some of them.
Write a simple algorithm to decide on when to buy and sell stocks to maximize profit
What's your most challenging project?
Why do you want to join?
Have you ever considered having a pet and why?
Connaissez-vous les valeurs de l'entreprise ?
Data Science Interview (I only remember 8/9 questions): 1. What is cross-validation, where it is used, how do you do it correctly? 2. What are the differences between supervised and unsupervised algorithms? 3. What are examples of structured vs unstructured data? 4. What is multi-collinearity, why is it bad and how do we deal with it? 5. What is data normalization and what are the reasons behind doing it? 6. How would you handle NaNs and outliers? 7. Describe the life-cycle of a data science project 8. What are various evaluation metrics in machine learning and how would you choose between each one of them? Coding Interview: 1. Print all numbers between 1 and n, omitting multiples of 5 and 7. 2. Variant of the maximum number overlapping intervals problem 3. Write a program to compute the TF-IDF scores of all tokens in a given corpus of documents Behavioral Interview: Typical questions that you can find online, nothing unexpected Data Science Interview Detailed discussion of my past projects Mini Data Science Project I was given some data and asked to explore the dataset and fit a model to predict a certain target on future data points. A simple regression problem. I was given 1,5 hours.
Questions about AB Testing and churn predictions.
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