The world of data science is becoming increasingly competitive, especially when it comes to landing a coveted position at top tech companies like Tinder, Airbnb, or Instacart. Mastering data scientist interview questions can make all the difference. In this comprehensive guide, we’ll break down some of the most common and challenging data science interview problems you might face, specifically targeting questions relevant to Tinder and similar tech companies. We’ll explain the underlying concepts, and show you real code and SQL solutions. Let’s dive in!

Data Scientist Interview Questions – Tinder

1. AB Test Analysis with Unbalanced Sample Sizes

Scenario: You are analyzing the results of an AB test. Variant A has a sample size of 50,000 users, and Variant B has 200,000 users. Should you be concerned about bias due to the unbalanced sample sizes? (Similar to Airbnb interview questions)

Understanding AB Test Design

• Randomization: Users must be randomly assigned to each group to ensure that observed differences are due to the variant, not confounding factors.
• Sample Size: The number of users in each group. Ideally, groups are of equal size, but unbalanced groups are sometimes unavoidable.

Does Unbalanced Sample Size Cause Bias?

The primary concern in AB testing is whether the randomization process was fair. If users were randomly assigned, the difference in sample size does not inherently create bias. The statistical test (e.g., t-test) will account for different sample sizes in the calculation of standard error.

• Variance: The group with the smaller sample size (50K) will have a higher variance, leading to a larger standard error for its estimate.
• Power: The test will have less statistical power compared to a balanced design, especially for detecting small effects.

Key Takeaway

• If the assignment was random, no bias is introduced solely by sample size imbalance.
• However, statistical power is reduced, and confidence intervals will be wider for the smaller group.
• Consider using statistical tests that account for unequal group sizes (like Welch’s t-test).
• Bias can arise if randomization was not performed correctly, or if group assignment is correlated with user characteristics.

Conclusion

Unbalanced sample sizes do not bias the results as long as the assignment is random. The main effect is reduced statistical power and precision in the smaller group.

2. Removing Stop Words from a String (Instacart)

Scenario: Given a list of stop words, write a function that takes a string and returns a new string with the stop words removed. This is a common text-processing question, especially in search and recommendation systems like Instacart.

What are Stop Words?

Stop words are commonly used words (such as "the", "is", "in") that are often filtered out in natural language processing (NLP) tasks because they carry little meaningful content.

Python Solution

def remove_stop_words(text, stop_words):
    """
    Removes stop words from a string.

    Args:
        text (str): The input string.
        stop_words (set or list): Set or list of stop words to be removed.

    Returns:
        str: The string with stop words removed.
    """
    # Convert the stop words to a set for fast lookup
    stop_words_set = set(stop_words)
    # Split the text into words
    words = text.split()
    # Filter out stop words
    filtered_words = [word for word in words if word.lower() not in stop_words_set]
    # Join the words back into a string
    return ' '.join(filtered_words)

# Example usage
stop_words = {'the', 'is', 'at', 'which', 'on', 'and'}
text = "The quick brown fox jumps over the lazy dog on the hill"
result = remove_stop_words(text, stop_words)
print(result)  # Output: "quick brown fox jumps over lazy dog hill"

Explanation

• Splitting the string by whitespace extracts all the words.
• Each word is converted to lowercase and checked against the stop words list/set.
• Non-stop words are joined back into a string.

3. SQL Query: Average Right Swipes by Feed Ranking Variant (Tinder)

Scenario: You have two tables:

• swipes: Holds a row for every Tinder swipe, with a boolean column is_right_swipe (true if right swipe, false if left swipe).
• variants: Records which user has which variant of an AB test.

You are to write a SQL query that outputs the average number of right swipes for two different variants of a feed ranking algorithm by comparing users who have swiped their first 10, 50, and 100 swipes.

Understanding the Problem

• We need to compute the average number of right swipes for each variant, for users who have at least 10, 50, and 100 swipes.
• For each user, only consider their first 10, 50, or 100 swipes.
• Then, aggregate by variant.

SQL Solution

WITH ranked_swipes AS (
    SELECT
        s.user_id,
        v.variant,
        s.is_right_swipe,
        ROW_NUMBER() OVER (PARTITION BY s.user_id ORDER BY s.swipe_time) AS swipe_rank
    FROM
        swipes s
    JOIN
        variants v ON s.user_id = v.user_id
)
, swipe_counts AS (
    SELECT
        user_id,
        variant,
        SUM(CASE WHEN swipe_rank <= 10 THEN is_right_swipe::int ELSE 0 END) AS right_swipes_10,
        SUM(CASE WHEN swipe_rank <= 50 THEN is_right_swipe::int ELSE 0 END) AS right_swipes_50,
        SUM(CASE WHEN swipe_rank <= 100 THEN is_right_swipe::int ELSE 0 END) AS right_swipes_100,
        MAX(swipe_rank) AS total_swipes
    FROM
        ranked_swipes
    GROUP BY
        user_id, variant
)
SELECT
    variant,
    AVG(right_swipes_10) AS avg_right_swipes_10,
    AVG(right_swipes_50) AS avg_right_swipes_50,
    AVG(right_swipes_100) AS avg_right_swipes_100
FROM
    swipe_counts
WHERE
    total_swipes >= 100 -- Only include users with at least 100 swipes
GROUP BY
    variant
ORDER BY
    variant

Step-by-Step Explanation

1. ranked_swipes: Assigns a rank to each swipe per user (using ROW_NUMBER()), ordered by swipe_time. This lets us identify the first 10, 50, and 100 swipes.
2. swipe_counts: For each user/variant, counts the number of right swipes in their first 10, 50, and 100 swipes using conditional aggregation.
3. Main SELECT: Aggregates these counts by variant, computing the average number of right swipes for each group.
4. Filtering: Only includes users who have at least 100 swipes for the 100-swipe analysis. Adjust WHERE clause for 10 or 50 as needed.

Concepts Involved

• Window Functions: ROW_NUMBER() to order swipes per user.
• Conditional Aggregation: Sum only for the first N swipes.
• Joins: To associate each swipe with its variant group.
• Group By: To compute averages per variant.

Summary & Key Takeaways

• Lifetime Value (LTV) Calculation: Multiply ARPU by average customer lifetime. Churn rate can be used to estimate lifetime if not directly observed.
• AB Testing with Unbalanced Groups: No bias if randomization is done correctly. Power and precision are affected, but results remain valid.
• Text Processing (Stop Word Removal): Use set operations and list comprehensions for efficient filtering in Python.
• SQL for Experiment Analysis: Use window functions and conditional aggregation to analyze user behaviors in AB tests.

Mastering these concepts and being able to explain them clearly in an interview will set you apart as a well-rounded data scientist, whether you’re targeting roles at Tinder or any other top tech company.

Frequently Asked Questions (FAQs)

• Q: Why is customer lifetime value important in SaaS?
  A: LTV helps you understand how much revenue you can expect from a customer, guiding marketing spend and product investment decisions.
• Q: What’s the best way to handle unbalanced AB test groups?
  A: Use statistical tests that accommodate unequal group sizes (like Welch’s t-test) and ensure randomization is strictly followed.
• Q: What are some common stop words?
  A: Examples include “the”, “is”, “in”, “at”, “on”, “and”, “a”, “an”.
• Q: Why use window functions in SQL?
  A: Window functions allow you to compute statistics (like ranks or running totals) across sets of rows related to the current row, which is essential for cohort or time-based analysis.

If you’re preparing for a data science interview at Tinder or a similar company, use these examples to hone your problem-solving skills and deepen your understanding of key data science concepts. Good luck!

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