A Data Scientist at Warner Bros. Discovery plays a pivotal role in shaping the future of global entertainment. Operating at the intersection of technology, media, and consumer behavior, you will leverage massive datasets to drive strategic decisions across premier streaming platforms like Max, legacy cable networks, and sports broadcasting. The insights you generate directly influence how content is created, distributed, and monetized, making this position highly visible and strategically vital to the company's long-term growth.

In this role, you will tackle complex, large-scale challenges that directly impact millions of active subscribers worldwide. Your day-to-day work will involve designing sophisticated personalization algorithms, optimizing content recommendation engines, predicting subscriber churn, and modeling user lifetime value. By translating complex behavioral data into actionable product features and business strategies, you help ensure that viewers remain engaged and that platform experiences are seamless and intuitive.

What makes this position exceptionally compelling is the sheer scale and variety of the data you will work with. From streaming clickstream data and user demographic profiles to marketing campaign metrics and content metadata, you will have access to a rich data ecosystem. To thrive at Warner Bros. Discovery, you must combine deep technical expertise in machine learning and statistical modeling with a keen product sense and the ability to communicate complex findings to non-technical stakeholders.

The questions you will encounter during the Warner Bros. Discovery selection process are designed to evaluate your technical precision, mathematical depth, and product intuition. While specific questions may vary depending on the team and seniority level, they are drawn from real interview experiences to highlight key patterns and core competencies that the hiring team prioritizes.

Coding & Data Manipulation

These questions assess your foundational programming skills, data structures knowledge, and your ability to manipulate and query datasets efficiently using Python and SQL.

• Write a function in Python to reverse a singly linked list.
• Explain the practical differences between a LEFT JOIN and a RIGHT JOIN, and describe a scenario where using one over the other changes the output dataset.

To succeed in the Warner Bros. Discovery interview process, you must approach your preparation with a structured strategy. The hiring team looks for well-rounded candidates who do not just write code, but who understand the business value of their models. Your preparation should balance technical execution with strategic communication.

You will be evaluated across four primary pillars, each critical to your success in the role:

Technical & Algorithmic Rigor – You must demonstrate a strong grasp of foundational computer science concepts, efficient coding practices in Python, and fluent database querying in SQL. Interviewers look for clean, optimized code and the ability to explain data structures and algorithmic complexity clearly.

Machine Learning Depth – You are expected to understand not just how to implement machine learning algorithms using standard libraries, but how they work under the hood. Be ready to explain the underlying mathematics, loss functions, optimization techniques, and the trade-offs of different modeling approaches.

Product & Causal Thinking – You need to connect data science methodologies to business outcomes. This involves understanding how to design robust experiments, formulate hypotheses, build recommendation systems, and apply causal inference techniques to measure user behavior and platform health.

Communication & Collaboration – As a Data Scientist, you will collaborate with cross-functional teams, including product managers, engineers, and creative executives. You must be able to articulate your technical choices, present data insights persuasively, and demonstrate strong alignment with the company's collaborative culture.

The interview process for a Data Scientist at Warner Bros. Discovery is comprehensive, typically taking between three to six weeks to complete. It is designed to evaluate both your technical execution and your high-level system design and behavioral alignment. The process moves from automated screening stages to highly interactive, deep-dive discussions with senior team members.

The journey begins with an initial screening phase. This often includes a virtual, on-demand recorded video interview (such as a Hirevue assessment) where you will answer a series of behavioral questions and basic technical prompts. In some locations and teams, this is accompanied by an online technical assessment covering basic computer science concepts, Python coding, aptitude, and fundamental machine learning questions. This phase is designed to establish a baseline of your communication skills and technical foundations.

If you pass the initial screening, you will move into the core technical rounds. This phase typically consists of two to four virtual interviews, which can sometimes be scheduled back-to-back. These rounds dive deep into Python data manipulation, SQL database querying, causal modeling case studies, and core machine learning theory. You will also engage in a project walkthrough round where you will present your previous technical achievements and face detailed questions regarding your engineering decisions and modeling choices.

The visual timeline above illustrates the typical progression of stages a candidate moves through during the hiring process. You should use this structure to pace your preparation, focusing heavily on core technical foundations early on, and shifting toward system design, case studies, and behavioral storytelling as you approach the final rounds. While the exact ordering of rounds can vary slightly based on team and location, this represents the standard evaluation framework.

Python Data Manipulation & SQL

This evaluation area tests your ability to clean, transform, and extract insights from large datasets. You must demonstrate proficiency in writing efficient, readable code and queries that can scale to production levels.

Be ready to go over:

• Pandas and NumPy optimization – Vectorization techniques, handling missing data, and avoiding common memory bottlenecks.
• SQL join mechanics – Deep understanding of inner, left, right, and full outer joins, and how null values affect join outputs.
• Aggregation and window functions – Writing complex analytical queries using window functions (PARTITION BY, ROW_NUMBER, LEAD, LAG).
• Advanced concepts (less common) – Query optimization, indexing strategies, and working with distributed computing frameworks like PySpark.

Example scenarios:

• "You are given a highly fragmented dataset of user streaming histories. Write a Python script using Pandas to deduplicate records, impute missing session lengths, and calculate the rolling 7-day active viewing time for each subscriber."
• "Write a SQL query to identify users who upgraded their subscription plan, calculating the exact number of days between their initial sign-up date and their upgrade date, grouped by marketing channel."

Machine Learning Theory & Derivations

This section assesses your mathematical maturity and your theoretical understanding of statistical learning. Interviewers want to see if you understand the mathematical mechanics behind the algorithms you deploy.

Be ready to go over:

• Regularization techniques – The mathematical formulations of L1 and L2 regularization, and how they prevent overfitting.
• Loss function formulation – Deriving and explaining loss functions for linear regression, logistic regression, and classification trees.
• Model evaluation and validation – Selecting appropriate validation schemes (e.g., stratified k-fold) and metrics for specific business problems.
• Advanced concepts (less common) – Optimization algorithms like gradient descent variants, learning rate schedules, and custom loss functions.

Example scenarios:

• "Explain the mathematical difference between Lasso and Ridge regression. Why does Lasso regression lead to sparse feature selection while Ridge does not?"
• "Walk me through how a Gradient Boosting machine constructs sequential trees. How does the loss function guide the creation of each subsequent tree?"

Recommendation Systems & Causal Modeling

This area is highly specific to the media and streaming context of Warner Bros. Discovery. It evaluates your ability to build personalized user experiences and measure the impact of platform changes.

Be ready to go over:

• Collaborative and content-based filtering – Designing algorithms to recommend movies and shows based on user viewing history and metadata.
• Causal inference – Utilizing techniques like propensity score matching, difference-in-differences, and synthetic controls to measure treatment effects.
• A/B testing design – Setting up rigorous experiments, choosing primary/secondary metrics, and determining statistical power.
• Advanced concepts (less common) – Deep learning-based recommendation models, multi-armed bandits for dynamic content layout, and network interference in testing.

Example scenarios:

• "Design a recommendation system for Max that balances showing highly popular content with personalizing recommendations for niche genres."
• "We want to test a new layout for the home screen of our app. Explain how you would design the A/B test, handle potential user interference, and measure the causal impact on long-term subscriber retention."

Algorithmic Problem Solving (DSA)

While this is a Data Scientist role rather than a pure software engineering role, you must still demonstrate solid algorithmic thinking and problem-solving skills.

Be ready to go over:

• Basic data structures – Arrays, strings, hash maps, linked lists, and trees.
• Search and sort algorithms – Binary search, sorting complexities, and basic graph traversals.
• Time and space complexity – Analyzing your code using Big O notation and identifying areas for optimization.
• Advanced concepts (less common) – Dynamic programming, recursion, and advanced graph algorithms.

Example scenarios:

• "Write an algorithm to reverse a singly linked list in-place, and explain its time and space complexity."
• "Given an array of integers representing video watch times, find the contiguous subarray that has the largest sum of watch times."