Data and Analytical Assessment
The analytical assessment is a critical component of the DoubleVerify interview loop, particularly for product teams managing core measurement metrics or data-heavy pipelines. This round is often conducted with a senior engineer or data analyst and focuses on your hands-on data skills.
You will be evaluated on your ability to look at raw data, write basic to intermediate SQL queries, and extract meaningful product insights. The team wants to see that you do not just rely on pre-built dashboards, but can actively query databases to investigate anomalies, track performance, and validate product hypotheses.
Be ready to go over:
- SQL Fundamentals – Writing queries involving joins, aggregations, and filters to analyze user behavior or system performance.
- Data Interpretation – Analyzing a dataset to identify trends, anomalies, or potential technical issues.
- Metrics Definition – Establishing key performance indicators (KPIs) for real-time data pipelines and measurement solutions.
- Advanced concepts (less common) – Familiarity with big data environments (e.g., BigQuery, Looker) and basic Python for data manipulation.
Example scenarios:
- "Given a database schema containing ad impression logs, write a query to calculate the viewability rate across different publishers."
- "How would you design an experiment to test a new ad fraud detection algorithm, and what metrics would you monitor to ensure its accuracy?"
- "Walk me through how you would investigate a sudden drop in a key measurement metric on a partner platform."
Take-Home Assignment & Case Study Presentation
For many Product Manager roles, DoubleVerify utilizes a take-home written assignment followed by a panel presentation. This stage is designed to simulate the actual day-to-day work of a PM at the company, testing your strategic thinking, product design capabilities, and execution detail.
You will typically be given a prompt related to a real-world business challenge, such as designing a new measurement capability or integrating with a new social platform surface. You will have a set amount of time (often a few days) to complete a written proposal or PRD, which you will then present to a panel consisting of PMs, engineers, and client-facing stakeholders.
Be ready to go over:
- Product Strategy – Defining the target audience, identifying customer pain points, and outlining a clear product vision.
- Technical Requirements – Writing detailed user stories, defining API requirements, and mapping out high-level data flows.
- Go-To-Market Planning – Collaborating with sales and marketing to position the product and drive scaled adoption.
- Advanced concepts (less common) – Addressing privacy regulations (e.g., GDPR, CCPA) and platform policy constraints in your product design.
Example scenarios:
- "Design a product requirements document (PRD) for a new attention-based measurement metric on a short-form video platform."
- "Present a roadmap for scaling our brand safety solutions to support emerging ad formats, outlining the key engineering and data science dependencies."
- "How would you prioritize feature requests from three major global advertisers who have conflicting requirements for a social integration product?"
Technical & Integration Strategy
Because DoubleVerify relies heavily on integrations with external platforms, walled gardens, and publisher environments, PMs must possess strong technical acumen. This round evaluates your comfort level working with complex systems, APIs, and data-driven architectures.
You do not need to write production code, but you must be able to hold your own in deep technical discussions with engineering and data science partners. The interviewers want to see that you can anticipate technical constraints, ask the right questions, and make informed architectural trade-offs.
Be ready to go over:
- API Design & Integrations – Understanding how data is exchanged between systems and how to design robust integration points.
- AI/ML-Powered Products – Working with data scientists to build, evaluate, and scale model-driven measurement products.
- System Constraints – Managing latency, data volume, and processing limits in real-time environments.
- Advanced concepts (less common) – Understanding SDK integrations, server-to-server tracking, and cryptographic verification methods.
Example scenarios:
- "Explain how you would partner with engineering to design an API integration that must handle millions of requests per second with minimal latency."
- "How do you evaluate the performance of an AI model used for content classification, and how do you handle false positives?"
- "What are the key technical challenges when building a measurement solution that spans open web, mobile apps, and connected TV (CTV) environments?"
