Data Science Interview Series Part 1: Acing the Square Data Scientist Interview

If you‘re preparing for data science interviews in 2024, you‘ve come to the right place. This is the first installment in my multi-part series on cracking the data science interview, with a special focus on interviewing for a data scientist role at Square.

Data science interviews at top tech companies like Square are notoriously rigorous and wide-ranging, covering topics like statistics, probability, linear algebra, machine learning, coding, and more. While the specific format and questions asked will vary by company and role, there are certain core competencies you‘ll need to demonstrate.

In this post, I‘ll share an inside look at what it takes to ace the Square data scientist interview in particular. We‘ll examine the key areas tested, walk through practice questions, and I‘ll share my top tips for standing out. Let‘s dive in!

Overview of the Square Data Scientist Interview

The data scientist interview process at Square typically consists of a phone screen followed by a virtual onsite loop with 4-5 back-to-back interviews. Depending on the specific team, you can expect to be evaluated in the following areas:

1. Product analytics and experimentation

As a data scientist at Square, you‘ll work closely with product managers, designers, and engineers to analyze product performance, run experiments, and drive key business metrics. Interviewers will probe your ability to transform ambiguous business questions into concrete experiments.

Sample question: How would you test whether a new checkout flow improves conversion rate?

To answer this, you‘ll want to walk through the steps of designing a controlled experiment:

1. Clearly define the objective and metrics
2. Decide on your randomization unit (user vs. session vs. device)
3. Determine the minimum detectable effect you care about
4. Calculate the required sample size for statistical significance
5. Discuss how you would bucket users into treatment vs. control
6. Mention guardrail metrics to monitor for adverse effects
7. Explain how you would analyze the results and draw conclusions

2. SQL and relational databases

Most of the data you‘ll interact with will live in relational databases like Snowflake or MySQL. Expect to be grilled on your SQL querying abilities and knowledge of database concepts.

Sample question: Write a query to identify users who have transacted at least 3 times in the past 30 days.

Here‘s one way to approach it:

SELECT user_id
FROM transactions 
WHERE created_at >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY user_id
HAVING COUNT(DISTINCT transaction_id) >= 3;

Make sure you‘re comfortable with JOINs, aggregations, window functions, and subqueries. Also review indexing, query optimization, and denormalization.

3. Statistical modeling and machine learning

While the data science role at Square is more analytics-focused, you will be expected to have foundational knowledge of stats and ML.

Sample question: What‘s the difference between L1 and L2 regularization?

L1 regularization, also known as Lasso regression, adds the absolute value of the model coefficients as a penalty term to the loss function. This results in sparse models where many coefficients are zeroed out, making it useful for feature selection.

In contrast, L2 regularization (Ridge regression), adds the squared magnitude of the coefficients to the loss function. This shrinks all coefficients towards zero but keeps them non-zero, making it better for reducing model variance without inducing sparsity.

Other key concepts to review: bias-variance tradeoff, cross-validation, feature engineering, model evaluation metrics, overfitting vs. underfitting.

4. Data pipelines and ETL

Square‘s data scientists are expected to be hands-on with building and maintaining data pipelines. Interviewers may ask about your experience with batch vs. streaming systems, data warehousing, and workflow management.

Sample question: Design a data pipeline to ingest transaction data from MySQL, transform it, and load it into a Redshift data warehouse.

A solid answer would cover:

• Using an ETL tool like Airflow to orchestrate the pipeline
• Extracting data from MySQL using the SQLAlchemy hook
• Performing transformations in memory with Pandas
• Loading data into staging tables on Redshift first
• Performing a merge operation to update the production tables
• Setting up data quality checks and monitoring

5. Coding questions

While coding is not the primary focus for data scientists at Square, you will face 1-2 technical programming interviews to assess your general problem-solving skills. Questions tend to center on data manipulation, algorithms, and data structures.

Sample question: Write a function to find the median of a stream of integers.

A brute force approach would be to maintain a sorted list of all numbers seen so far and return the middle element(s). However, this would be O(n log n) insertion time.

To optimize, we can use two heaps:

1. A max-heap to store the smaller half of the input
2. A min-heap to store the larger half

We‘ll keep the heaps balanced (off by at most one element). The median will then be either the top of the larger heap or the average of the top elements from both heaps, depending on if the total count is odd or even.

from heapq import heappush, heappop

class MedianFinder:

    def __init__(self):
        self.small = []  # max heap
        self.large = []  # min heap

    def addNum(self, num: int) -> None:
        # Add to max-heap (smaller half)
        heappush(self.small, -num)

        # Balance the heaps
        if self.small and self.large and -self.small[0] > self.large[0]:
            val = -heappop(self.small)
            heappush(self.large, val)

        # Ensure heaps are off by at most one element
        if len(self.small) > len(self.large) + 1:
            val = -heappop(self.small)
            heappush(self.large, val)
        if len(self.large) > len(self.small) + 1:
            val = heappop(self.large)
            heappush(self.small, -val)

    def findMedian(self) -> float:
        if len(self.small) > len(self.large):
            return -self.small[0] 
        if len(self.large) > len(self.small):
            return self.large[0]
        return (-self.small[0] + self.large[0]) / 2

The time complexity of the addNum operation is O(log n) due to the heap insertions and removals. Finding the median is O(1). The space complexity is O(n) to store the input.

6. Business case studies

Finally, expect 1-2 case study interviews, where you‘ll be presented with an open-ended business scenario and asked to analyze it both quantitatively and qualitatively.

Sample question: Square is considering launching a "buy now, pay later" feature for large purchases. How would you evaluate this opportunity?

There‘s no one right answer, but a solid response would cover:

• Clarifying the objective and constraints
• Estimating the size of the opportunity based on current transaction data
• Discussing potential risks and how to mitigate them
• Identifying key metrics to measure success
• Proposing an experiment to validate demand and willingness to pay
• Considering partnerships with existing BNPL providers

The key is demonstrating structured thinking, business acumen, and creativity. Don‘t be afraid to make assumptions and think out loud.

Tips for Preparing for the Square Data Scientist Interview

Now that you have a sense of what to expect, here are my top tips for preparing:

1. Practice, practice, practice! Work through as many real interview questions as you can. Start with websites like LeetCode and Stratascratch, then progress to Square-specific questions on Glassdoor.

2. Review fundamental concepts in stats, probability, linear algebra, and computer science. Don‘t just memorize formulas – make sure you can explain the intuition behind them.

3. Get comfortable with SQL. Most onsite interviews will involve live coding in a collaborative IDE. Write out queries by hand and practice talking through your thought process.

4. Brush up on your communication skills. Mock interview with friends or colleagues and focus on clearly explaining technical concepts.

5. Demonstrate passion for the payments/fintech space. Research Square‘s products and business model, and come prepared with thoughtful questions.

Conclusion

Interviewing for a data scientist role at Square is challenging but rewarding. By diligently preparing across the areas outlined in this post, you‘ll set yourself up for success.

Stay tuned for the next installments in this series, where we‘ll take a closer look at data science interviews at other top companies like Google, Facebook, and Amazon. In the meantime, keep practicing and don‘t get discouraged – landing that dream data science job is within reach!