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66 job interview questions for data scientists

We are now at 86 questions. These are mostly open-ended questions, to assess the technical horizontal knowledge of a senior candidate for a rather high level position, e.g. director.

  1. What is the biggest data set that you processed, and how did you process it, what were the results?
  2. Tell me two success stories about your analytic or computer science projects? How was lift (or success) measured?
  3. What is: lift, KPI, robustness, model fitting, design of experiments, 80/20 rule?
  4. What is: collaborative filtering, n-grams, map reduce, cosine distance?
  5. How to optimize a web crawler to run much faster, extract better information, and better summarize data to produce cleaner databases?
  6. How would you come up with a solution to identify plagiarism?
  7. How to detect individual paid accounts shared by multiple users?
  8. Should click data be handled in real time? Why? In which contexts?
  9. What is better: good data or good models? And how do you define "good"? Is there a universal good model? Are there any models that are definitely not so good?
  10. What is probabilistic merging (AKA fuzzy merging)? Is it easier to handle with SQL or other languages? Which languages would you choose for semi-structured text data reconciliation? 
  11. How do you handle missing data? What imputation techniques do you recommend?
  12. What is your favorite programming language / vendor? why?
  13. Tell me 3 things positive and 3 things negative about your favorite statistical software.
  14. Compare SAS, R, Python, Perl
  15. What is the curse of big data?
  16. Have you been involved in database design and data modeling?
  17. Have you been involved in dashboard creation and metric selection? What do you think about Birt?
  18. What features of Teradata do you like?
  19. You are about to send one million email (marketing campaign). How do you optimze delivery? How do you optimize response? Can you optimize both separately? (answer: not really)
  20. Toad or Brio or any other similar clients are quite inefficient to query Oracle databases. Why? How would you do to increase speed by a factor 10, and be able to handle far bigger outputs? 
  21. How would you turn unstructured data into structured data? Is it really necessary? Is it OK to store data as flat text files rather than in an SQL-powered RDBMS?
  22. What are hash table collisions? How is it avoided? How frequently does it happen?
  23. How to make sure a mapreduce application has good load balance? What is load balance?
  24. Examples where mapreduce does not work? Examples where it works very well? What are the security issues involved with the cloud? What do you think of EMC's solution offering an hybrid approach - both internal and external cloud - to mitigate the risks and offer other advantages (which ones)?
  25. Is it better to have 100 small hash tables or one big hash table, in memory, in terms of access speed (assuming both fit within RAM)? What do you think about in-database analytics?
  26. Why is naive Bayes so bad? How would you improve a spam detection algorithm that uses naive Bayes?
  27. Have you been working with white lists? Positive rules? (In the context of fraud or spam detection)
  28. What is star schema? Lookup tables? 
  29. Can you perform logistic regression with Excel? (yes) How? (use linest on log-transformed data)? Would the result be good? (Excel has numerical issues, but it's very interactive)
  30. Have you optimized code or algorithms for speed: in SQL, Perl, C++, Python etc. How, and by how much?
  31. Is it better to spend 5 days developing a 90% accurate solution, or 10 days for 100% accuracy? Depends on the context?
  32. Define: quality assurance, six sigma, design of experiments. Give examples of good and bad designs of experiments.
  33. What are the drawbacks of general linear model? Are you familiar with alternatives (Lasso, ridge regression, boosted trees)?
  34. Do you think 50 small decision trees are better than a large one? Why?
  35. Is actuarial science not a branch of statistics (survival analysis)? If not, how so?
  36. Give examples of data that does not have a Gaussian distribution, nor log-normal. Give examples of data that has a very chaotic distribution?
  37. Why is mean square error a bad measure of model performance? What would you suggest instead?
  38. How can you prove that one improvement you've brought to an algorithm is really an improvement over not doing anything? Are you familiar with A/B testing?
  39. What is sensitivity analysis? Is it better to have low sensitivity (that is, great robustness) and low predictive power, or the other way around? How to perform good cross-validation? What do you think about the idea of injecting noise in your data set to test the sensitivity of your models?
  40. Compare logistic regression w. decision trees, neural networks. How have these technologies been vastly improved over the last 15 years?
  41. Do you know / used data reduction techniques other than PCA? What do you think of step-wise regression? What kind of step-wise techniques are you familiar with? When is full data better than reduced data or sample?
  42. How would you build non parametric confidence intervals, e.g. for scores? (see the AnalyticBridge theorem)
  43. Are you familiar either with extreme value theory, monte carlo simulations or mathematical statistics (or anything else) to correctly estimate the chance of a very rare event?
  44. What is root cause analysis? How to identify a cause vs. a correlation? Give examples.
  45. How would you define and measure the predictive power of a metric?
  46. How to detect the best rule set for a fraud detection scoring technology? How do you deal with rule redundancy, rule discovery, and the combinatorial nature of the problem (for finding optimum rule set - the one with best predictive power)? Can an approximate solution to the rule set problem be OK? How would you find an OK approximate solution? How would you decide it is good enough and stop looking for a better one?
  47. How to create a keyword taxonomy?
  48. What is a Botnet? How can it be detected?
  49. Any experience with using API's? Programming API's? Google or Amazon API's? AaaS (Analytics as a service)?
  50. When is it better to write your own code than using a data science software package?
  51. Which tools do you use for visualization? What do you think of Tableau? R? SAS? (for graphs). How to efficiently represent 5 dimension in a chart (or in a video)?
  52. What is POC (proof of concept)?
  53. What types of clients have you been working with: internal, external, sales / finance / marketing / IT people? Consulting experience? Dealing with vendors, including vendor selection and testing?
  54. Are you familiar with software life cycle? With IT project life cycle - from gathering requests to maintenance? 
  55. What is a cron job? 
  56. Are you a lone coder? A production guy (developer)? Or a designer (architect)?
  57. Is it better to have too many false positives, or too many false negatives?
  58. Are you familiar with pricing optimization, price elasticity, inventory management, competitive intelligence? Give examples. 
  59. How does Zillow's algorithm work? (to estimate the value of any home in US)
  60. How to detect bogus reviews, or bogus Facebook accounts used for bad purposes?
  61. How would you create a new anonymous digital currency?
  62. Have you ever thought about creating a startup? Around which idea / concept?
  63. Do you think that typed login / password will disappear? How could they be replaced?
  64. Have you used time series models? Cross-correlations with time lags? Correlograms? Spectral analysis? Signal processing and filtering techniques? In which context?
  65. Which data scientists do you admire most? which startups?
  66. How did you become interested in data science?
  67. What is an efficiency curve? What are its drawbacks, and how can they be overcome?
  68. What is a recommendation engine? How does it work?
  69. What is an exact test? How and when can simulations help us when we do not use an exact test?
  70. What do you think makes a good data scientist?
  71. Do you think data science is an art or a science?
  72. What is the computational complexity of a good, fast clustering algorithm? What is a good clustering algorithm? How do you determine the number of clusters? How would you perform clustering on one million unique keywords, assuming you have 10 million data points - each one consisting of two keywords, and a metric measuring how similar these two keywords are? How would you create this 10 million data points table in the first place?
  73. Give a few examples of "best practices" in data science.
  74. What could make a chart misleading, difficult to read or interpret? What features should a useful chart have?
  75. Do you know a few "rules of thumb" used in statistical or computer science? Or in business analytics?
  76. What are your top 5 predictions for the next 20 years?
  77. How do you immediately know when statistics published in an article (e.g. newspaper) are either wrong or presented to support the author's point of view, rather than correct, comprehensive factual information on a specific subject? For instance, what do you think about the official monthly unemployment statistics regularly discussed in the press? What could make them more accurate?
  78. Testing your analytic intuition: look at these three charts. Two of them exhibit patterns. Which ones? Do you know that these charts are called scatter-plots? Are there other ways to visually represent this type of data?
  79. You design a robust non-parametric statistic (metric) to replace correlation or R square, that (1) is independent of sample size, (2) always between -1 and +1, and (3) based on rank statistics. How do you normalize for sample size? Write an algorithm that computes all permutations of n elements. How do you sample permutations (that is, generate tons of random permutations) when n is large, to estimate the asymptotic distribution for your newly created metric? You may use this asymptotic distribution for normalizing your metric. Do you think that an exact theoretical distribution might exist, and therefore, we should find it, and use it rather than wasting our time trying to estimate the asymptotic distribution using simulations? 
  80. More difficult, technical question related to previous one. There is an obvious one-to-one correspondence between permutations of n elements and integers between 1 and n! Design an algorithm that encodes an integer less than n! as a permutation of n elements. What would be the reverse algorithm, used to decode a permutation and transform it back into a number? Hint: An intermediate step is to use the factorial number system representation of an integer. Feel free to check this reference online to answer the question. Even better, feel free to browse the web to find the full answer to the question (this will test the candidate's ability to quickly search online and find a solution to a problem without spending hours reinventing the wheel).  
  81. How many "useful" votes will a Yelp review receive? My answer: Eliminate bogus accounts (read this article), or competitor reviews (how to detect them: use taxonomy to classify users, and location - two Italian restaurants in same Zip code could badmouth each other and write great comments for themselves). Detect fake likes: some companies (e.g. FanMeNow.com) will charge you to produce fake accounts and fake likes. Eliminate prolific users who like everything, those who hate everything. Have a blacklist of keywords to filter fake reviews. See if IP address or IP block of reviewer is in a blacklist such as "Stop Forum Spam". Create honeypot to catch fraudsters.  Also watch out for disgruntled employees badmouthing their former employer. Watch out for 2 or 3 similar comments posted the same day by 3 users regarding a company that receives very few reviews. Is it a brand new company? Add more weight to trusted users (create a category of trusted users).  Flag all reviews that are identical (or nearly identical) and come from same IP address or same user. Create a metric to measure distance between two pieces of text (reviews). Create a review or reviewer taxonomy. Use hidden decision trees to rate or score review and reviewers.
  82. What did you do today? Or what did you do this week / last week?
  83. What/when is the latest data mining book / article you read? What/when is the latest data mining conference / webinar / class / workshop / training you attended? What/when is the most recent programming skill that you acquired?
  84. What are your favorite data science websites? Who do you admire most in the data science community, and why? Which company do you admire most?
  85. What/when/where is the last data science blog post you wrote? 
  86. In your opinion, what is data science? Machine learning? Data mining?
  87. Who are the best people you recruited and where are they today?
  88. Can you estimate and forecast sales for any book, based on Amazon public data? Hint: read this article.
  89. What's wrong with this picture?
  90. Should removing stop words be Step 1 rather than Step 3, in the search engine algorithm described here? Answer: Have you thought about the fact that mine and yours could also be stop words? So in a bad implementation, data mining would become data mine after stemming, then data. In practice, you remove stop words before stemming. So Step 3 should indeed become step 1. 
  91. Experimental design and a bit of computer science with Lego's

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Comment by Pradyumna S. Upadrashta on June 15, 2014 at 4:28am

Case in point:  http://sfglobe.com/?id=411&src=share_fb_new_411

These answers show some kind of genius.

Comment by Pradyumna S. Upadrashta on June 14, 2014 at 10:34pm

I think it is more insightful to probe deeply into a specific project that the candidate has previously worked on (e.g., incorporating some of these questions into the probe in the context of a specific project). First, these questions out of context can overwhelm good candidates in an already nerve-wracking interview setting. Second, if the interviewer is terrible with English, or inept at structuring their thoughts properly, they may be asking a question in a way that is very misleading, or asking the wrong questions altogether.  Does that make the interviewee wrong (/ a failure) because they couldn't answer a poorly worded (/ thought out) question in a high-pressure, time-limited situation? For senior roles (manager, director, etc.), I think its important to have previous technical experience, or you risk hiring a dud / disaster waiting to happen.

Comment by Joshua Weiner on April 12, 2014 at 3:53pm

What is the answer to question 37? What is wrong with mean square error? As long as you are looking at the MSE on the test set... and using it compare models, then I think it is a perfectly fine measure.

Comment by vishali rajiv on November 18, 2013 at 11:35pm

@Vincent

can i get the possible answers for the above interview questions

Vishali

Comment by Vincent Granville on September 12, 2013 at 9:26am

I have added one new question - question #90.

Comment by Vincent Granville on May 5, 2013 at 7:44pm

Someone wrote: 

Looks like hiring managers expect data scientists to have expertise in machine learning, statistics, business intelligence, database design, data munging, data visualization and programming. Are not these requirements too excessive?

My answer:

I think being familiar with all these domains (add computer science, map reduce) is necessary, as well as expertise in some of these domains. Mastering two programming languages (Java, Python) is a must, as well as familiarity with R and SQL. Visualization is easy to acquire.


Knowing how to quickly and independently find, learn (or if necessary, invent) and assess usefulness of the techniques needed to handle the problems, is critical, and MORE important than "knowing" the techniques in the first place. A good amount of experience with some techniques is necessary. 

But you don't need to be an expert in everything. For instance, about 90% of what I learned in statistics courses, I've never had to use it to solve business problems. So why learn it in the first place? Also, machine learning (in my opinion) is a subset of statistics focusing on clustering, pattern recognition and association rules. 

The mistake that many hiring managers do is looking for someone who is an expert in everything.

Comment by Joe M on April 21, 2013 at 5:06pm

Are questions like this actually asked in high-level interviews?  All I ever got when I was starting out was "What was your most satisfying experience?" and "Other than for the money, why do you want this job?"

Comment by Ritendra on February 25, 2013 at 12:44pm

Vincent,

Que are good and many of them are based on practical exp too.

Thx for sharing the comments from Allen Engelhardt, it provides better context.

Answer to #76, I would luv to see #1 VA -> Visual Analytics being top of them.

#2 should be, large amount of the data replaced by video's.

Comment by Mars Ma on February 20, 2013 at 5:51am

@Vincent Granville, really useful questions, I like them, thanks a lot !~~

Comment by Amy on February 19, 2013 at 11:40am

@Craig: If the client returns result in your browser, you can handle only as much data as your browser can. In most cases, a 80,000 row table will crash your browser. Just access Oracle directly via Python or Perl, and you can handle (extract and save) gigabytes of data quite easily. And far, far faster.

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