Chartboost Data Scientist Interview Questions + Guide in 2025

Chartboost is a leading in-app monetization and programmatic advertising platform that empowers mobile app developers and advertisers through its comprehensive suite of services.

As a Data Scientist at Chartboost, you will play a critical role within the pricing team, focusing on the development of machine learning models that optimize real-time bidding processes. Key responsibilities include applying, developing, and benchmarking machine learning approaches to enhance AI-driven products, conducting large-scale A/B testing, and analyzing vast amounts of structured and unstructured data to derive meaningful insights. You will work collaboratively with product, data, and engineering teams to implement new pricing and conversion models that handle hundreds of thousands of requests per second.

To excel in this position, you should possess strong skills in statistics and probability, as well as experience with programming languages such as Python and SQL. Familiarity with distributed frameworks like Spark is also advantageous. The ideal candidate is proactive, detail-oriented, and has a solid understanding of machine learning principles, along with the ability to communicate complex data insights effectively.

This guide will assist you in preparing for the interview by providing insights into the skills and qualities that Chartboost values, helping you to tailor your responses and showcase your fit for the role.

The interview process for a Data Scientist role at Chartboost is structured to assess both technical and interpersonal skills, ensuring candidates are well-suited for the dynamic environment of the company. The process typically consists of several key stages:

1. Initial Screening

The first step is an initial screening, which usually takes place via a phone call with a recruiter or the hiring manager. This conversation focuses on your background, experience, and motivation for applying to Chartboost. The recruiter will also provide insights into the company culture and the specifics of the Data Scientist role, allowing both parties to gauge mutual fit.

2. Technical Assessment

Following the initial screening, candidates are often required to complete a technical assessment. This may involve a take-home coding challenge or a live coding interview, where you will demonstrate your proficiency in Python, SQL, and relevant data science concepts. Expect to tackle problems related to statistics, algorithms, and machine learning, as well as showcase your ability to analyze and interpret data effectively.

3. Onsite Interviews

The onsite interview typically consists of multiple rounds, often four, where candidates meet with various team members, including data scientists, product managers, and engineering leads. Each interview lasts around 45 minutes and covers a mix of technical and behavioral questions. You may be asked to solve case studies, discuss your previous projects, and explain your approach to data analysis and model development. Collaboration and communication skills are also evaluated, as these are crucial for working effectively within cross-functional teams.

4. Final Interview

In some cases, a final interview may be conducted with senior management or executives. This round focuses on your long-term vision, alignment with Chartboost's goals, and your potential contributions to the team. It’s an opportunity for you to ask questions about the company’s direction and culture, as well as to demonstrate your enthusiasm for the role.

As you prepare for your interview, consider the specific skills and experiences that will be relevant to the questions you may encounter. Next, we will delve into the types of questions that candidates have faced during the interview process.

Here are some tips to help you excel in your interview.

Understand the Interview Structure

The interview process at Chartboost typically consists of multiple rounds, including an initial screening with HR, a technical assessment, and a final round with various team members. Familiarize yourself with this structure so you can prepare accordingly. Expect to demonstrate your technical skills, particularly in coding and data analysis, as well as your ability to collaborate across teams.

Showcase Your Technical Proficiency

As a Data Scientist, you will be expected to have a strong command of statistics, algorithms, and programming languages like Python and SQL. Brush up on your knowledge of statistical methods and machine learning techniques, as these will be crucial in your role. Be prepared to discuss your experience with real-time data processing and optimization algorithms, as well as your familiarity with distributed frameworks like Spark.

Prepare for Problem-Solving Questions

Expect to encounter problem-solving questions that assess your analytical thinking and logical reasoning. For example, you might be asked to estimate the number of baseball bats in the U.S. or to explain how you would support other teams with their challenges. Practice articulating your thought process clearly and concisely, as interviewers will be interested in how you approach complex problems.

Emphasize Collaboration and Communication Skills

Chartboost values collaboration across different teams, so be ready to discuss your experience working with product managers, engineers, and other stakeholders. Highlight instances where you successfully communicated complex data insights to non-technical audiences. This will demonstrate your ability to bridge the gap between data science and business needs.

Be Ready for Behavioral Questions

Behavioral questions are common in interviews at Chartboost. Prepare to share examples from your past experiences that showcase your problem-solving abilities, teamwork, and adaptability. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey the impact of your contributions.

Show Enthusiasm for the Company and Role

Chartboost is looking for candidates who are passionate about data and its potential to drive intelligent decisions. Research the company’s products and recent developments in the ad tech space, and be prepared to discuss how your skills and experiences align with their mission. Express your excitement about the opportunity to contribute to their innovative projects.

Practice Coding and Technical Assessments

Given the technical nature of the role, you may be required to complete coding challenges or technical assessments. Practice coding problems on platforms like LeetCode or HackerRank, focusing on algorithms and data structures. Additionally, be prepared to discuss your previous projects and the methodologies you used to achieve results.

Follow Up with Thoughtful Questions

At the end of the interview, you will likely have the opportunity to ask questions. Use this time to inquire about the team dynamics, the challenges they face, and how success is measured in the role. This not only shows your interest in the position but also helps you gauge if Chartboost is the right fit for you.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Scientist role at Chartboost. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Chartboost. The interview process will likely focus on your technical skills in statistics, machine learning, and programming, as well as your ability to collaborate with cross-functional teams. Be prepared to demonstrate your problem-solving abilities and your understanding of data-driven decision-making.

Statistics and Probability

1. How would you explain the concept of p-value to a non-technical stakeholder?

Understanding how to communicate complex statistical concepts is crucial in a collaborative environment.

How to Answer

Use simple language and relatable examples to explain the p-value, emphasizing its role in hypothesis testing.

Example

“A p-value helps us determine the strength of our evidence against a null hypothesis. If we have a p-value of 0.05, it means there’s a 5% chance that we would see our results if the null hypothesis were true. In simpler terms, a low p-value suggests that our findings are statistically significant and not just due to random chance.”

2. Can you describe a time when you conducted an A/B test? What were the results?

A/B testing is a common practice in data science, especially in product development.

How to Answer

Discuss the objective of the test, the methodology, and the outcomes, focusing on how the results influenced decision-making.

Example

“I conducted an A/B test to evaluate two different pricing strategies for our app. We randomly assigned users to either the control group with the original pricing or the test group with a new pricing model. The results showed a 20% increase in conversion rates for the test group, leading us to implement the new pricing strategy across the board.”

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

Understanding errors in hypothesis testing is fundamental for a data scientist.

How to Answer

Clearly define both types of errors and provide examples to illustrate the differences.

Example

“A Type I error occurs when we incorrectly reject a true null hypothesis, essentially a false positive. For instance, concluding that a new feature improves user engagement when it actually does not. A Type II error, on the other hand, happens when we fail to reject a false null hypothesis, or a false negative, like missing out on a beneficial feature because our test didn’t show significant results.”

4. How do you handle missing data in a dataset?

Handling missing data is a common challenge in data analysis.

How to Answer

Discuss various strategies for dealing with missing data, such as imputation, deletion, or using algorithms that support missing values.

Example

“I typically assess the extent and pattern of missing data first. If it’s minimal, I might use mean or median imputation. For larger gaps, I consider using predictive models to estimate missing values or even dropping those records if they don’t significantly impact the analysis.”

Machine Learning

1. Can you explain the difference between supervised and unsupervised learning?

This question tests your foundational knowledge of machine learning techniques.

How to Answer

Define both terms and provide examples of each to illustrate their applications.

Example

“Supervised learning involves training a model on labeled data, where we know the outcome, such as predicting house prices based on features like size and location. Unsupervised learning, however, deals with unlabeled data, where the model tries to find patterns or groupings, like clustering customers based on purchasing behavior.”

2. Describe a machine learning project you worked on. What challenges did you face?

This question assesses your practical experience and problem-solving skills.

How to Answer

Outline the project, your role, the challenges encountered, and how you overcame them.

Example

“I worked on a project to predict user churn for a mobile app. One challenge was dealing with imbalanced classes, as most users did not churn. I addressed this by using techniques like SMOTE for oversampling the minority class and adjusting the model’s threshold to improve recall without sacrificing precision.”

3. How do you evaluate the performance of a machine learning model?

Understanding model evaluation metrics is crucial for data scientists.

How to Answer

Discuss various metrics and when to use them, such as accuracy, precision, recall, and F1 score.

Example

“I evaluate model performance based on the specific problem at hand. For classification tasks, I often look at precision and recall to understand the trade-offs between false positives and false negatives. For regression tasks, I might use RMSE or R-squared to assess how well the model predicts outcomes.”

4. What techniques do you use for feature selection?

Feature selection is vital for improving model performance and interpretability.

How to Answer

Mention techniques like recursive feature elimination, LASSO regression, or tree-based methods.

Example

“I often use recursive feature elimination to systematically remove features and assess model performance. Additionally, I might apply LASSO regression, which penalizes less important features, helping to identify the most impactful variables for the model.”

Programming and Data Manipulation

1. How do you optimize SQL queries?

Optimizing queries is essential for efficient data retrieval.

How to Answer

Discuss techniques such as indexing, avoiding SELECT *, and using joins effectively.

Example

“I optimize SQL queries by ensuring that I use indexes on columns frequently used in WHERE clauses. I also avoid using SELECT * and instead specify only the columns I need. Additionally, I analyze query execution plans to identify bottlenecks and adjust my queries accordingly.”

2. Describe your experience with Python for data analysis.

Python is a key tool for data scientists, and familiarity with libraries is important.

How to Answer

Mention specific libraries and your experience using them for data manipulation and analysis.

Example

“I have extensive experience using Python for data analysis, particularly with libraries like Pandas for data manipulation and NumPy for numerical computations. I often use Matplotlib and Seaborn for data visualization to communicate insights effectively.”

3. Can you explain how you would handle a large dataset that doesn’t fit into memory?

Handling large datasets is a common challenge in data science.

How to Answer

Discuss techniques such as chunking, using Dask, or leveraging databases.

Example

“When dealing with large datasets, I often use chunking to process the data in smaller batches. Alternatively, I might leverage Dask for parallel computing or use SQL databases to perform operations directly on the data without loading it all into memory.”

4. How do you ensure the quality of your data?

Data quality is critical for accurate analysis and modeling.

How to Answer

Discuss methods for data validation, cleaning, and monitoring.

Example

“I ensure data quality by implementing validation checks during data ingestion, such as checking for duplicates and missing values. I also perform regular audits and use automated scripts to monitor data quality over time, ensuring that any anomalies are addressed promptly.”