Preparing for an interview at Quantcast requires a structured approach that balances theoretical knowledge with practical execution. The evaluation process is rigorous, and success depends on demonstrating depth across several core competencies.

Quantitative and Statistical Rigor – You must possess a deep understanding of probability, hypothesis testing, and statistical modeling. Interviewers will push you to explain the mathematical foundations of your approaches, not just the high-level concepts. Be ready to write out equations and walk through proofs for fundamental concepts like conditional probability.

SQL Proficiency at Scale – You need to be highly fluent in SQL, as you will be tested on your ability to manipulate large datasets under tight time limits. This includes a strong grasp of joins, subqueries, window functions, and query optimization techniques.

Problem-Solving under Pressure – The technical assessments are intentionally designed to be challenging to complete in the allotted time. You must demonstrate the ability to quickly analyze a problem, structure a solution, and execute it accurately without getting bogged down in minor details.

Domain Curiosity – While prior ad tech experience is highly beneficial, a strong candidate must at least demonstrate a rapid capacity to learn the mechanics of the digital advertising industry. Showing an understanding of real-time bidding, audience segmentation, and attribution will set you apart.

The interview process for the Research Analyst position at Quantcast is thorough and highly technical. It is designed to filter for candidates who possess strong data science capabilities alongside practical analytical skills. Recruiters are highly collaborative and provide feedback throughout the process, but the technical bars are set high.

The journey begins with an initial recruiter phone screen, which focuses on your background, career interests, and alignment with the company's culture. Following this, you will face a highly demanding technical assessment stage, which typically includes a comprehensive written or take-home test focused on SQL and advanced statistics. If you pass this stage, you will move on to a technical phone screen with a hiring manager or senior analyst to dive deeper into your mathematical and modeling expertise, followed by a multi-round onsite interview.

The timeline above outlines the typical progression from the initial application to the final decision. Candidates should expect the technical test and the subsequent phone screen to be the most demanding phases of the process. Managing your preparation time effectively between SQL practice and statistical theory is critical to maintaining momentum through these early stages.

To excel in the Quantcast interview, you must understand the specific execution standards expected in each core evaluation area.

Advanced Statistics and Probability

This area is often the primary filter for candidates. Quantcast relies on sophisticated statistical models to make real-time decisions, and they expect their analysts to have a rock-solid mathematical foundation.

Be ready to go over:

• Conditional Probability – Detailed calculations and real-world applications of probability theory.
• Bayes Rule – Solving classic Bayesian problems and explaining how prior beliefs are updated with new data.
• Hypothesis Testing – Setting up power analyses, understanding p-values, and designing robust experiments.
• Advanced concepts (less common) – Markov chains, stochastic processes, and advanced Bayesian inference.

Example questions or scenarios:

• "Given a click-through rate distribution, how would you prove whether a observed 5% increase in CTR is statistically significant?"
• "Walk me through the mathematical proof of Bayes Theorem and explain its relevance to machine learning classification tasks."

SQL and Large-Scale Data Manipulation

The take-home and written tests will evaluate your ability to write clean, efficient SQL queries against substantial datasets. You may be asked to import massive tables into your local environment to complete the test.

Be ready to go over:

• Window Functions – Using ROW_NUMBER(), RANK(), LEAD(), and LAG() to analyze sequential user events.
• Data Aggregation – Complex groupings, rollups, and handling null values in large datasets.
• Query Optimization – Understanding indexing, execution plans, and how to write queries that run efficiently on tables with millions of rows.

Example questions or scenarios:

• "Write a query to identify users who visited a specific landing page but did not make a purchase within the subsequent 48 hours."
• "Optimize a query containing multiple nested subqueries that is timing out on a 1,000,000-row user event table."

Machine Learning and Mathematical Optimization

Because the Research Analyst role borders on data science, you will be evaluated on your understanding of predictive modeling and optimization algorithms.

Be ready to go over:

• Linear Programming – Formulating objective functions and constraints to solve resource allocation problems.
• Supervised Learning – Deep understanding of regression, classification, and decision-tree-based models.
• Model Evaluation – Precision, recall, ROC-AUC curves, and cross-validation techniques.

Example questions or scenarios:

• "How would you formulate a linear programming model to maximize ad campaign conversions given strict budget constraints across different channels?"
• "Explain how you would handle highly collinear features in a regression model designed to predict user lifetime value."

Advertising Domain Knowledge

Understanding how the digital advertising ecosystem works is crucial for contextualizing the data you will be working with.

Be ready to go over:

• Real-Time Bidding (RTB) – How ad exchanges, demand-side platforms (DSPs), and supply-side platforms (SSPs) interact.
• Attribution Models – The pros and cons of first-touch, last-touch, and multi-touch attribution.
• Ad Metrics – Thorough understanding of CPM, CPC, CPA, CTR, and eCPM.

Example questions or scenarios:

• "If a cookie-less browser environment limits user tracking, how would you design an alternative methodology to measure audience reach?"
• "Explain the difference between first-price and second-price auctions and how they affect bidding strategies."