Zynga Data Scientist Interview Questions + Guide in 2025

Zynga is a leading developer of popular social games, engaging millions of players worldwide with titles such as FarmVille, Words With Friends, and Zynga Poker.

As a Data Scientist at Zynga, you will play a crucial role in leveraging data to drive decision-making across marketing, publishing, and product functions. Your responsibilities will include constructing complex machine learning models, developing innovative data pipelines, and applying statistical methodologies to assess performance and manage uncertainties. You will work within a modern tech stack involving AWS, Databricks, and Airflow while implementing continuous integration and delivery processes to ensure efficiency in your solutions. The ideal candidate will possess a strong statistical background, proficiency in SQL and Python, and a mindset geared towards innovative problem-solving and architectural design. With the autonomy to lead key initiatives, you will be expected to demonstrate technical excellence and a collaborative spirit in a data-driven environment.

This guide is designed to help you prepare for your interview by providing insights into the expectations and requirements of the Data Scientist role at Zynga, ensuring you can articulate your skills and experiences effectively.

The interview process for a Data Scientist role at Zynga is structured to assess both technical skills and cultural fit within the company. It typically consists of several stages, each designed to evaluate different aspects of a candidate's qualifications and experience.

1. Initial Phone Screen

The process begins with a 30-minute phone interview with a recruiter or a Data Science Lead. This initial conversation focuses on your background, relevant projects, and how your skills align with the role. Expect to answer basic SQL questions, as well as some introductory statistics and probability concepts. This stage is crucial for determining if you meet the foundational requirements for the position.

2. Technical Assessment

Following the initial screen, candidates usually undergo a technical assessment, which may take place over a platform like CoderPad. This assessment typically includes SQL exercises, where you will be asked to perform tasks involving joins, aggregations, and other data manipulation techniques. Additionally, you may encounter programming questions that test your knowledge of Python and machine learning concepts. This stage is designed to evaluate your technical proficiency and problem-solving abilities.

3. Onsite Interviews

Candidates who successfully pass the technical assessment are invited for onsite interviews, which can last up to five hours. This stage consists of multiple one-on-one interviews with various team members, including data scientists, leads, and managers. During these interviews, you will face a mix of technical questions covering statistics, modeling, and advanced SQL, as well as behavioral questions to assess your fit within the team and company culture. You may also be asked to whiteboard programming problems or discuss your approach to real-world data challenges.

4. Final Evaluation

In some cases, there may be a final evaluation stage where candidates are asked to complete a take-home test or project. This test is designed to simulate real work scenarios and assess your ability to apply your skills in a practical context. Be prepared for this to take longer than initially indicated, as the complexity of the tasks may require more time than expected.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may arise during the process.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Zynga. The interview process will likely focus on your technical skills in SQL, statistics, machine learning, and Python, as well as your ability to apply these skills in a gaming context. Be prepared to demonstrate your problem-solving abilities and your understanding of data-driven decision-making in the gaming industry.

SQL

1. Can you explain the difference between INNER JOIN and LEFT JOIN in SQL?

Understanding the nuances of SQL joins is crucial for data manipulation and retrieval.

How to Answer

Discuss the definitions of both INNER JOIN and LEFT JOIN, emphasizing how they differ in terms of the data they return from the tables involved.

Example

"An INNER JOIN returns only the rows where there is a match in both tables, while a LEFT JOIN returns all rows from the left table and the matched rows from the right table. If there is no match, NULL values are returned for columns from the right table."

2. How would you optimize a slow-running SQL query?

Performance optimization is key in data-heavy environments.

How to Answer

Talk about indexing, query structure, and analyzing execution plans to identify bottlenecks.

Example

"I would start by examining the execution plan to identify slow operations. Then, I would consider adding indexes on columns used in WHERE clauses or JOIN conditions. Additionally, I would review the query structure to eliminate unnecessary subqueries or joins."

3. Describe a scenario where you used SQL to solve a business problem.

Real-world application of SQL skills is essential.

How to Answer

Provide a specific example that highlights your analytical skills and the impact of your work.

Example

"In my previous role, I used SQL to analyze user engagement data, which revealed that a significant portion of users dropped off after the first week. This insight led to the development of a targeted retention campaign that increased user engagement by 20%."

4. What are window functions in SQL, and when would you use them?

Window functions are powerful tools for data analysis.

How to Answer

Explain what window functions are and provide an example of their application.

Example

"Window functions allow you to perform calculations across a set of table rows related to the current row. For instance, I used a window function to calculate the running total of user purchases over time, which helped in understanding purchasing trends."

Statistics & Probability

1. Explain the concept of p-value in hypothesis testing.

Statistical knowledge is critical for data analysis.

How to Answer

Define p-value and its significance in hypothesis testing.

Example

"The p-value measures the strength of evidence against the null hypothesis. A low p-value indicates that the observed data is unlikely under the null hypothesis, leading us to reject it in favor of the alternative hypothesis."

2. What is the difference between Type I and Type II errors?

Understanding errors in hypothesis testing is fundamental.

How to Answer

Discuss both types of errors and their implications.

Example

"A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. Balancing these errors is crucial in statistical analysis, especially in decision-making processes."

3. Can you describe a situation where you applied statistical methods to analyze data?

Practical application of statistics is essential.

How to Answer

Share a specific example that demonstrates your statistical analysis skills.

Example

"I conducted a regression analysis to determine the factors affecting user retention in a mobile game. By identifying key predictors, we were able to implement changes that improved retention rates by 15%."

4. What is logistic regression, and when would you use it?

Logistic regression is a common statistical method in data science.

How to Answer

Explain logistic regression and its applications.

Example

"Logistic regression is used for binary classification problems, where the outcome is a binary variable. I used it to predict whether users would churn based on their engagement metrics, which helped in developing targeted retention strategies."

Machine Learning

1. Describe a machine learning project you have worked on.

Demonstrating hands-on experience is vital.

How to Answer

Outline the project, your role, and the impact of the results.

Example

"I worked on a project to predict user churn using a random forest model. By analyzing user behavior data, we identified at-risk users and implemented targeted interventions, resulting in a 25% reduction in churn."

2. What are some common metrics used to evaluate machine learning models?

Understanding model evaluation is crucial for data scientists.

How to Answer

Discuss various metrics and their relevance.

Example

"Common metrics include accuracy, precision, recall, F1 score, and AUC-ROC. For instance, in a classification problem, I prioritize precision and recall to ensure that we minimize false positives and negatives, especially in a gaming context where user experience is critical."

3. How do you handle overfitting in machine learning models?

Addressing overfitting is essential for model performance.

How to Answer

Explain techniques to prevent overfitting.

Example

"I use techniques such as cross-validation, regularization, and pruning to prevent overfitting. For example, in a decision tree model, I applied pruning to reduce complexity and improve generalization on unseen data."

4. Can you explain the concept of feature engineering?

Feature engineering is a key aspect of building effective models.

How to Answer

Define feature engineering and its importance.

Example

"Feature engineering involves creating new input features from existing data to improve model performance. In a project analyzing user engagement, I created features like session length and frequency of play, which significantly enhanced the model's predictive power."