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Data Scientist - Interview Questions

Table of contents

1. Interview Questions

1.1. Data Scientist

  • [Facebook] Product Generalist (i.e. solving a business case study)
    • How to design the friends you may know feature -- how to recommend friends;
    • How can we tell if two users on Instagram are best friends
    • How would you build a model to decide what to show a user and how would you evaluate its success
    • How would you create a model to find bad sellers on marketplace?
  • [Facebook] Coding Exercise (in SQL): joins (LEFT, RIGHT, UNION), group by, date manipulation
  • [Facebook] Quantitative Analysis
    • How to test out the assumptions; how to decide next steps if the metrics shows only positive signals in certain features
    • How can you tell if your model is working?
  • [Facebook] Applied Data (stats questions): AB Testing

1.2. Machine Learning Engineer

  • Coding Interview:
    • [Data Structure] Difference Stack vs Queue, Dequeue Implementation, Linked List Reversal
    • [Easy] Reverse a linked list, Convert decimal to hexadecimal without using built-in methods (str, int etc.), pairs of number that sum up to K
    • [Medium] Verify binary search tree
    • [Hard] Min edit distance
  • Techinical Interview:
    • Fundamental ML questions:
      • Non-deep and deep methods
      • Basic ML models or algorithms: Formula for gradient descent, Linear an Non-Linear classifiers, K-means, Random forest, Clustering Nearest neighbors. Decision Tree
      • Basic DL: Explain how CNN works, Recurrent neural network
      • Metric Understanding: ROC
      • What is overfitting?
      • Difference between Bagging and Boosting
      • Regularization: Diff of L1 and L2 regularization
    • System Design:
      • How to search efficiently
      • Given salaries of people from ten professions and salary of a new people. Design an algorithm to predict the profession of this new people.
    • Case Study:
      • How would you apply A/B testing on food odering service
      • How surge pricing works for both customers and drivers
      • Implement Huffman code for a given English sentence
  • Interview with Hiring Manager: explain your Machine learning projects

1.3. Grab: Machine Learning Engineer

  • General mobility industry and economics oriented questions

  • How surge pricing work for both customers and drivers?

  • Formula for gradient decent

  • Supervised and unsupervised ML methods, detailed question about different classification and clustering algos

  • What is overfitting and how you deal with it

  • How to solve the issue if the features are highly correlated?

  • What is a good way to detect anomalies?

  • What's the ROC Curve? What does an ROC curve plot?

  • What's the difference between bagging and boosting?

  • How do you find out average number of bookings for a given day. What factors do you think will play a crucial role?

  • How do you thing grab can implement surge pricing concept different than that to Uber. What factors do you think will play a role here ?

2. SQL Questions

SQL#1: Facebook

Given the following data:

Table:
searches
Columns:
date STRING date of the search,
search_id INT the unique identifier of each search,
user_id INT the unique identifier of the searcher,
age_group STRING ('<30', '30-50', '50+'),
search_query STRING the text of the search query

Sample Rows:
date | search_id | user_id | age_group | search_query
--------------------------------------------------------------------
'2020-01-01' | 101 | 9991 | '<30' | 'justin bieber'
'2020-01-01' | 102 | 9991 | '<30' | 'menlo park'
'2020-01-01' | 103 | 5555 | '30-50' | 'john'
'2020-01-01' | 104 | 1234 | '50+' | 'funny cats'


Table:
search_results
Columns:
date STRING date of the search action,
search_id INT the unique identifier of each search,
result_id INT the unique identifier of the result,
result_type STRING (page, event, group, person, post, etc.),
clicked BOOLEAN did the user click on the result?

Sample Rows:
date | search_id | result_id | result_type | clicked
--------------------------------------------------------------------
'2020-01-01' | 101 | 1001 | 'page' | TRUE
'2020-01-01' | 101 | 1002 | 'event' | FALSE
'2020-01-01' | 101 | 1003 | 'event' | FALSE
'2020-01-01' | 101 | 1004 | 'group' | FALSE


Over the last 7 days, how many users made more than 10 searches?

You notice that the number of users that clicked on a search result
about a Facebook Event increased 10% week-over-week. How would you
investigate? How do you decide if this is a good thing or a bad thing?

The Events team wants to up-rank Events such that they show up higher
in Search. How would you determine if this is a good idea or not?

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