Admarketplace Data Scientist Interview Questions + Guide in 2025

Admarketplace is dedicated to delivering engaging consumer search experiences while empowering advertisers to measure media performance accurately across a variety of platforms.

The Data Scientist role at Admarketplace involves leveraging expertise in machine learning operations (ML Ops), reliability, and performance monitoring to enhance machine learning models within a high-performance search advertising ecosystem. In this position, you will collaborate with machine learning engineers, product managers, and data engineers to create and maintain a scalable ML infrastructure that aligns with business objectives such as yield optimization and improved advertiser ROI. Your responsibilities will include monitoring model accuracy through dashboards and reports, identifying performance regressions, and implementing effective model monitoring frameworks.

A successful candidate will have a strong background in quantitative fields, extensive experience with large-scale ML systems in production, and proficiency in programming languages such as Python and SQL. You should also possess excellent communication skills to translate complex insights for both technical and non-technical stakeholders. Familiarity with the advertising domain and experience with ML frameworks will be advantageous.

This guide will help you prepare for your interview by providing insights into the expectations for the Data Scientist role at Admarketplace, enabling you to articulate your relevant experiences confidently and effectively.

The interview process for a Data Scientist at adMarketplace is structured to assess both technical skills and cultural fit within the organization. It typically consists of several key stages:

1. Initial Phone Screen

The process begins with a 30-minute phone interview with a recruiter or HR representative. This initial conversation is designed to gauge your interest in the role and the company, as well as to discuss your background and experiences. Expect questions that explore your previous work, particularly in relation to data science and machine learning, and how you align with adMarketplace's core values of curiosity, collaboration, and commitment.

2. Technical Assessment

Following the initial screen, candidates are usually required to complete a technical assessment. This may involve a take-home assignment that allows you to demonstrate your proficiency in relevant programming languages such as Python or SQL, as well as your understanding of machine learning concepts. You will typically have a few days to complete this assignment, which may include tasks like building models or analyzing datasets.

3. Live Technical Interview

After successfully completing the take-home assessment, candidates will participate in a live technical interview. This session often focuses on SQL and may include problem-solving exercises that test your analytical skills and ability to work with data in real-time. You may be asked to solve specific technical problems or to discuss your approach to data-related challenges.

4. Onsite Interview

The final stage of the interview process is an onsite interview, which can last several hours. This stage typically includes multiple rounds of interviews with team members, including data scientists, machine learning engineers, and product managers. Expect a mix of technical questions, case studies, and behavioral questions that assess your ability to collaborate and communicate effectively within cross-functional teams. You may also be asked to present your previous work or discuss how you would approach specific challenges relevant to adMarketplace's business objectives.

As you prepare for your interview, consider the types of questions that may arise in these stages, particularly those that relate to your technical expertise and your ability to contribute to adMarketplace's mission.

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

Understand the Interview Structure

The interview process at adMarketplace typically includes a phone screen, a take-home assessment, a live SQL work session, and a final interview. Familiarize yourself with each stage and prepare accordingly. For the take-home assessment, ensure you allocate enough time to showcase your skills in Python, SQL, or R, as you will be expected to demonstrate your technical proficiency.

Showcase Your Technical Skills

Given the emphasis on ML Ops and performance monitoring, be prepared to discuss your experience with large-scale ML systems and frameworks like TensorFlow or PyTorch. Brush up on your SQL skills, as you will likely face a technical assessment that tests your ability to solve problems using SQL. Practice common SQL queries and be ready to explain your thought process during the live session.

Emphasize Collaboration and Communication

adMarketplace values collaboration and effective communication across teams. Be prepared to discuss your experience working in cross-functional teams, particularly how you have bridged gaps between data engineering, product management, and machine learning. Highlight specific examples where your communication skills led to successful project outcomes or resolved conflicts.

Prepare for Behavioral Questions

Expect behavioral questions that assess your alignment with the company’s core values: Curiosity, Collaboration, Creative Conflict, Commitment, and Competitiveness. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Think of examples that demonstrate your curiosity in learning new technologies, your commitment to team success, and how you handle creative conflict in a collaborative environment.

Be Ready for Case Studies

The final interview may include case studies or problem-solving scenarios. Practice articulating your thought process clearly and logically. Focus on how you would approach model monitoring, performance regression, or A/B testing in a real-world context. This will demonstrate your ability to apply theoretical knowledge to practical challenges.

Show Enthusiasm for Continuous Learning

adMarketplace promotes a culture of continuous improvement and learning. Express your enthusiasm for professional development and how you stay updated with industry trends and best practices in ML Ops. Mention any relevant courses, certifications, or projects that showcase your commitment to growth in this field.

Ask Insightful Questions

Prepare thoughtful questions that reflect your understanding of the role and the company. Inquire about the team dynamics, the tools and technologies they use, or how they measure success in their ML models. This not only shows your interest in the position but also helps you gauge if the company culture aligns with your values.

By following these tips and tailoring your preparation to the specific expectations of adMarketplace, you will position yourself as a strong candidate for the Data Scientist role. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at adMarketplace. The interview process will likely assess your technical skills, problem-solving abilities, and your experience in machine learning operations, particularly in the context of advertising and performance monitoring.

Machine Learning and Model Evaluation

1. Can you explain the concept of model drift and how you would detect it?

Understanding model drift is crucial for maintaining model performance over time.

How to Answer

Discuss the importance of monitoring model performance metrics and the techniques you would use to identify shifts in data distribution or model accuracy.

Example

“Model drift occurs when the statistical properties of the target variable change over time, leading to a decline in model performance. I would implement a monitoring framework that tracks key performance indicators and use statistical tests to detect significant changes in model predictions compared to historical data.”

2. Describe your experience with A/B testing and how you would design an experiment.

A/B testing is essential for validating model assumptions and measuring performance improvements.

How to Answer

Explain the steps you would take to design a robust A/B test, including sample size determination, control and treatment group selection, and metrics for success.

Example

“I have designed A/B tests to evaluate the impact of new features on click-through rates. I ensure that the sample size is statistically significant and that the groups are randomized. I focus on metrics like conversion rates and user engagement to assess the effectiveness of the changes.”

3. How do you approach feature selection for a machine learning model?

Feature selection is critical for model performance and interpretability.

How to Answer

Discuss the methods you use for feature selection, such as correlation analysis, recursive feature elimination, or model-based approaches.

Example

“I typically start with exploratory data analysis to identify potential features. I then use techniques like recursive feature elimination and LASSO regression to select the most impactful features while avoiding overfitting.”

4. What metrics do you consider when evaluating the performance of a machine learning model?

Choosing the right metrics is vital for understanding model effectiveness.

How to Answer

Mention specific metrics relevant to the business context, such as accuracy, precision, recall, F1 score, and AUC-ROC, and explain why they matter.

Example

“I focus on metrics like precision and recall for classification tasks, especially in advertising, where false positives can be costly. For regression models, I look at RMSE and R-squared to assess how well the model predicts outcomes.”

SQL and Data Manipulation

1. How would you write a SQL query to find the average CTR for different ad placements?

SQL skills are essential for data extraction and analysis.

How to Answer

Outline the SQL functions you would use and the structure of your query.

Example

“I would use a query like: SELECT placement_id, AVG(CTR) as average_ctr FROM ad_data GROUP BY placement_id; This would give me the average click-through rate for each ad placement, allowing for performance comparisons.”

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

Understanding SQL joins is fundamental for data manipulation.

How to Answer

Clarify the differences in how these joins operate and when to use each.

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 matched rows from the right table, filling in NULLs where there are no matches. I use INNER JOIN when I only need matching records and LEFT JOIN when I want to retain all records from the primary table.”

3. Describe a complex SQL query you have written and its purpose.

This question assesses your ability to handle complex data scenarios.

How to Answer

Provide context for the query, the challenge it addressed, and the outcome.

Example

“I wrote a complex SQL query to analyze user engagement over time, which involved multiple joins and subqueries to aggregate data from user interactions, ad placements, and conversion rates. This helped identify trends in user behavior and informed our ad targeting strategy.”

Behavioral and Situational Questions

1. Tell me about a time you had to work with a challenging client or stakeholder.

Collaboration and communication are key in cross-functional teams.

How to Answer

Share a specific example that highlights your problem-solving and interpersonal skills.

Example

“I worked with a stakeholder who was skeptical about the effectiveness of our ML models. I organized a presentation to explain our methodologies and shared case studies demonstrating past successes. This helped build trust and led to a collaborative effort in refining our models.”

2. How do you prioritize tasks when working on multiple projects?

Time management is crucial in a fast-paced environment.

How to Answer

Discuss your approach to prioritization, including any frameworks or tools you use.

Example

“I prioritize tasks based on their impact on business goals and deadlines. I use tools like Trello to visualize my workload and ensure that I’m focusing on high-impact projects while also allowing time for unexpected issues that may arise.”

3. Describe a situation where you had to advocate for a new tool or process.

This question assesses your ability to drive change and innovation.

How to Answer

Explain the situation, your reasoning, and the outcome of your advocacy.

Example

“I noticed that our model monitoring process was inefficient, leading to delays in identifying performance issues. I researched and proposed the adoption of MLflow for better tracking and management of our models. After presenting the benefits to the team, we implemented it, which significantly improved our response time to model drifts.”