Data science interviews at top tech companies like Dropbox are known for their challenging and insightful questions. These questions not only test your technical knowledge and SQL or programming skills but also your ability to think analytically and communicate your reasoning clearly. In this comprehensive guide, we’ll walk through some of the most common and tricky data science interview questions—with a special focus on Dropbox—and provide detailed solutions and explanations. Whether you’re preparing for a data scientist, data analyst, or analytics engineering role, these questions will help you understand the types of concepts and skills you’ll need to demonstrate.

Data Science Interview Questions – Dropbox

1. SQL Problem: Students with Closest SAT Scores

Question: Given a table of students and their SAT test scores, write a query to return the two students with the closest test scores with the score difference. Assume a random pick if there are multiple students with the same score difference.

1.1 Understanding the Problem

Let’s break this down:

• We have a table (students) containing student names and their SAT scores.
• We need to find the pair of students whose scores are closest (i.e., the minimum absolute difference between any two scores).
• If multiple pairs have the same minimum difference, pick one randomly.

This is a classic “closest pair” problem, often seen in SQL interviews at Dropbox and similar tech companies.

1.2 Schema Assumption

Suppose the students table looks like this:

CREATE TABLE students (
    student_id INT PRIMARY KEY,
    student_name VARCHAR(50),
    sat_score INT
);

1.3 Approach and Concepts Involved

• We need to compare each student’s score with every other student’s score (a self-join).
• We want the absolute difference between every pair’s scores.
• Exclude pairs where the students are the same person.
• Find the pair(s) with the minimum difference.
• Return only one such pair (randomly if there are ties).

1.4 SQL Solution

SELECT
  s1.student_name AS student_1,
  s2.student_name AS student_2,
  ABS(s1.sat_score - s2.sat_score) AS score_diff
FROM
  students s1
JOIN
  students s2
    ON s1.student_id < s2.student_id
ORDER BY
  score_diff ASC
LIMIT 1;

1.5 Step-by-Step Explanation

• Self-Join: We join students table to itself. The condition s1.student_id < s2.student_id ensures:
  • We don’t compare a student with themselves.
  • We don’t duplicate pairs (A,B) and (B,A).
• Score Difference: ABS(s1.sat_score - s2.sat_score) gives us the absolute difference.
• Ordering and Limiting: We order by score_diff ascending to get the smallest difference at the top.
• Random Selection (if Ties): The above query picks one pair randomly in case of ties since LIMIT 1 arbitrarily chooses the first row. If you want to be more explicit, you could use ORDER BY RANDOM() among the ties.

1.6 Handling Multiple Ties (Optional Enhancement)

Suppose you want to explicitly handle ties by randomly picking among them:

WITH score_diffs AS (
  SELECT
    s1.student_name AS student_1,
    s2.student_name AS student_2,
    ABS(s1.sat_score - s2.sat_score) AS score_diff
  FROM
    students s1
  JOIN
    students s2
      ON s1.student_id < s2.student_id
),
min_diff AS (
  SELECT MIN(score_diff) AS min_score_diff
  FROM score_diffs
)
SELECT
  student_1,
  student_2,
  score_diff
FROM
  score_diffs
WHERE
  score_diff = (SELECT min_score_diff FROM min_diff)
ORDER BY
  RANDOM()
LIMIT 1;

1.7 Time Complexity and Performance

• This approach has \( O(N^2) \) time complexity for \( N \) students, since every student is compared to every other.
• For large tables, further optimization or window functions may be needed, but for interview purposes, this is sufficient.

1.8 Summary Table Example

Expected result: Alice and Dana with score_diff = 5, or Bob and Dana also with score_diff = 5 (either pair is acceptable).

2. Analytics Question: Decreasing Comments per User (Pinterest)

Question: Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city. The company has been consistently growing new users in the city from January to March. What are some reasons on why the average number of comments per user would be decreasing and what metrics would you look into?

2.1 Understanding the Problem

• We see a decrease in average comments per user over time, despite user growth.
• This is a classic analytics/metrics question, testing your product thinking and ability to generate hypotheses.

2.2 Key Concepts

• Average Comments per User (ACPU): \[ \text{ACPU} = \frac{\text{Total Number of Comments}}{\text{Total Number of Users}} \]
• Active vs. Total Users: Not all users are equally active. Growth in users doesn’t mean growth in engaged users.
• Cohort Analysis: Comparing behavior of new users vs. old users.
• Engagement Metrics: Comments per active user, retention rate, DAU/MAU, etc.

2.3 Possible Reasons for Decreasing ACPU

• New Users Are Less Engaged: The new users added from January to March may comment less than earlier users.
  • Early adopters are often more enthusiastic and engaged.
  • Recent users may be exploring or not yet comfortable with commenting.
• Inactive Users Increasing: User base is growing, but many are not active or not commenting.
• Product Changes: UI or UX changes may have made commenting harder or less visible.
• Content Quality or Relevance: Users may have less interesting content to comment on.
• External Factors: Seasonality, local events, or competitor launches affecting engagement.

2.4 Metrics to Investigate

• Active Users: Number and proportion of users who commented at least once per week.
• Comments per Active User: Is engagement dropping among engaged users, or is it just more inactive users?
  • \[ \text{Average Comments per Active User} = \frac{\text{Total Comments}}{\text{Number of Users Who Commented}} \]
• Cohort Analysis: Compare commenting behavior of users who joined in January vs. February vs. March.
• Retention Rate: Are new users sticking around and continuing to comment?
• Comments per Session/Visit: Are users commenting less per session?
• UI/UX Experiments: Did any product changes roll out that could affect commenting?
• Content Analysis: Has the amount or type of content changed?

2.5 Example Analytic Queries

-- Average comments per user per week
SELECT
  week_start,
  COUNT(DISTINCT user_id) AS num_users,
  COUNT(comment_id) AS num_comments,
  COUNT(comment_id) / COUNT(DISTINCT user_id) AS avg_comments_per_user
FROM
  comments
WHERE
  city = 'NewCity'
GROUP BY
  week_start
ORDER BY
  week_start;

-- Comments per active user per week
SELECT
  week_start,
  COUNT(DISTINCT CASE WHEN comment_id IS NOT NULL THEN user_id END) AS num_active_users,
  COUNT(comment_id) AS num_comments,
  COUNT(comment_id) / COUNT(DISTINCT CASE WHEN comment_id IS NOT NULL THEN user_id END) AS avg_comments_per_active_user
FROM
  comments
WHERE
  city = 'NewCity'
GROUP BY
  week_start
ORDER BY
  week_start;

2.6 Structuring Your Response in Interviews

• Start by restating the problem in your own words.
• List possible causes, prioritizing based on likelihood and impact.
• Suggest specific metrics and analyses to validate your hypotheses.
• Mention potential next steps or experiments if time allows.

2.7 Example Answer (Interview Style)

“There are several reasons why the average number of comments per user could be decreasing, even as the user base grows. One likely reason is that new users may be less engaged than early adopters, dragging down the overall average. It’s also possible that many new users are inactive, or that product changes have unintentionally made commenting less accessible.

To investigate, I would look at metrics like comments per active user, cohort analysis of new versus existing users, and retention rates. I’d also examine any product or content changes over this period. This would help identify whether the decline is due to disengaged new users, changes in user behavior, or other factors.”

3. Python Coding: Bigrams Function (Indeed)

Question: Write a function that can take a string and return a list of bigrams.

3.1 Understanding Bigrams

• A bigram is a sequence of two adjacent elements from a string or list of words. In natural language processing, bigrams are often used for text analysis and modeling.
• For example, the bigrams of the sentence “Data Science is fun” are: [‘Data Science’, ‘Science is’, ‘is fun’].

3.2 Implementation in Python

def get_bigrams(text):
    """
    Returns a list of bigrams from the input string.
    Each bigram is a string of two consecutive words joined by a space.
    """
    words = text.split()
    bigrams = []
    for i in range(len(words) - 1):
        bigram = words[i] + ' ' + words[i+1]
        bigrams.append(bigram)
    return bigrams

# Example usage:
print(get_bigrams("Data Science is fun"))
# Output: ['Data Science', 'Science is', 'is fun']

3.3 Explanation

• Splitting: text.split() splits the string into words.
• Looping: Iterate from 0 to len(words) - 2 to create pairs.
• Construction: Each bigram is formed by concatenating words[i] and words[i+1].

3.4 Handling Edge Cases

• If the input string has fewer than 2 words, return an empty list.
• For punctuation and case sensitivity, further preprocessing may be needed depending on use case.

3.5 Alternative Using List Comprehension

def get_bigrams(text):
    words = text.split()
    return [words[i] + ' ' + words[i+1] for i in range(len(words) - 1)]

3.6 Application in Data Science

• Bigrams are widely used in text mining, feature engineering, and NLP tasks such as sentiment analysis, language modeling, and topic modeling.
• Can be extended to trigrams (three-word sequences), n-grams, and more.

Summary and Tips for Dropbox Data Science Interviews

• SQL Skills: Be comfortable with self-joins, window functions, and aggregation. Practice writing queries that involve pairwise comparisons, deduplication, and ranking.
• Analytics Thinking: Always break down metrics questions into hypothesizing causes and suggesting concrete metrics or analyses. Use cohort analysis, funnel analysis, and behavioral

  segmentation techniques to support your hypotheses. Communicate your thought process clearly, structuring your answer logically—this is as important as technical correctness in a real interview.

  • Python Coding: Practice writing clean, efficient, and readable code for data manipulation and simple NLP tasks. Be ready to discuss edge cases, scalability, and how to adapt your solution for production use.
  • Math & Statistics: Expect questions involving basic probability, distributions, hypothesis testing, and metric definitions. Be prepared to explain concepts and calculations, often using Mathjax notation:
    \[ \text{Average Metric} = \frac{\sum_{i=1}^{n} x_i}{n} \]
  • Product Sense: Especially at companies like Dropbox, you may be asked about how you would measure product success, design A/B tests, or interpret experiment results.

  ---

  Dropbox Data Science Interview Preparation Strategies

  1. Mastering SQL and Data Manipulation

  SQL is the backbone of many data roles at Dropbox. Practice complex joins, window functions, and writing efficient queries. For the “closest pair” type of problem:

  • Always clarify requirements: Should duplicate pairs be excluded? How to handle ties?
  • Be able to explain the time complexity and potential optimizations for large datasets.
  • Use CTEs (WITH statements) for readability and modular query design.

  2. Analytics and Metrics Thinking

  Dropbox, like many growth-focused companies, is interested in your ability to reason about why metrics change and what that means for the business. When confronted with behavioral trends:

  • Break down aggregate metrics (like average or median) into their component parts.
  • Use cohort analysis to separate new user behavior from that of existing users.
  • Always ask: “What additional data would I need to confirm or refute my hypothesis?”

  3. Coding and Algorithmic Thinking

  While Dropbox interviews are not algorithm-heavy compared to pure software engineering roles, you may still face code tasks, especially around data processing and manipulation:

  • Practice writing concise functions for text processing, array manipulation, and simple statistics.
  • Be ready to explain your code and discuss potential improvements or edge cases.
  • If time allows, mention how you’d scale your code for large datasets (e.g., using generators, parallel processing).

  4. Communicating Your Solutions

  Dropbox values clear and effective communication. When you answer an interview question:

  • Restate the problem to show understanding.
  • Walk through your thought process step by step.
  • Explain trade-offs and why you chose a particular approach.
  • Summarize your final answer succinctly.

  ---

  Practice Questions for Dropbox Data Science Interviews

  Here are a few more questions to practice and deepen your understanding:

  • SQL: Given a table of user activity logs, find the users who have logged in at least once every week for the past three months.
  • Analytics: The conversion rate on a key Dropbox signup flow has dropped by 10% in the last month. What analyses would you conduct to diagnose the cause?
  • Python: Write a function to count the frequency of each trigram (three-word sequence) in a document.

  ---

  Frequently Asked Questions (FAQs)

  What is the structure of a Dropbox data science interview?

  You can expect a technical screen (SQL, Python, statistics), an analytics case round (open-ended metrics or product questions), and a final onsite with a mix of technical and behavioral questions.

  How do I prepare for SQL interviews at Dropbox?

  Practice complex queries with joins, window functions, and self-joins. Be ready to write queries on a whiteboard or shared document and explain your logic.

  How should I approach open-ended analytics questions?

  Break down the problem, generate hypotheses, suggest specific metrics, and explain how you’d validate each hypothesis. Show structured thinking and business acumen.

  What programming languages should I know?

  Python is the most common for coding rounds, especially for data manipulation and simple algorithms. R and SQL are also valuable, depending on the team.

  How important is product sense for Dropbox data science roles?

  Very important. Dropbox values candidates who can connect data insights to user behavior and business outcomes. Practice articulating the “so what?” of your analyses.

  ---

  Key Takeaways

  • Dropbox data science interviews test SQL, analytics, programming, and communication skills.
  • For SQL questions, focus on clean logic, efficiency, and correctness.
  • For analytics, break down metrics, hypothesize causes, and suggest targeted analyses.
  • For coding, write clear, testable functions—think about edge cases and explain your approach.
  • Always communicate your reasoning step by step.

  ---

  Additional Resources for Dropbox Interview Preparation

  • Dropbox Careers
  • LeetCode SQL and Python practice
  • Kaggle for data science problems and datasets
  
   

  ---

  Conclusion

  Preparing for a Dropbox data science interview requires a blend of technical skill, business intuition, and clear communication. By practicing real interview questions like those above—covering SQL pairwise analysis, behavioral analytics, and Python text processing—you’ll be well-equipped to succeed. Always remember to structure your answers, justify your choices, and connect your technical work to broader business impacts. Good luck!

  Related Articles

  • Data Science Interview Questions - Microsoft
  • Data Science Interview Question - Retail
  • Data Science Interview Question - Banking
  • Data Science Interview Question - Ecommerce
  • Data Science Interview Question - Healthcare