A Data Scientist at NBCUniversal plays a pivotal role in shaping the future of media and entertainment. Working within teams like the Peacock Data and Decision Sciences Team, you will apply advanced statistical modeling, machine learning, and data engineering to drive critical business decisions. From predicting subscriber churn and optimizing customer lifetime value (CLV) to building personalization algorithms and recommendation engines, your work directly impacts millions of viewers globally.

The scale of NBCUniversal's data ecosystem is massive, spanning streaming platforms, cable networks, theatrical releases, and theme parks. As a Data Scientist, you are not just building models in isolation; you are translating complex data into actionable strategies that influence content acquisition, marketing spend, and product user experience. This blend of massive scale, strategic business influence, and cutting-edge machine learning makes the role both highly challenging and immensely rewarding.

To succeed, you must possess strong technical foundations in SQL and Python, alongside the business acumen required to connect algorithmic outputs to bottom-line results. Whether you are optimizing ad-targeting algorithms or designing Retrieval-Augmented Generation (RAG) systems to improve internal workflows, your contributions will help define how one of the world's leading media companies leverages data to entertain and inform.

The following questions are representative of what you can expect during the NBCUniversal interview process. Because NBCUniversal matches candidates to specific teams (such as the Peacock streaming division or central analytics), the exact composition of your technical rounds will depend on the team you are interviewing with. Use these questions to identify patterns in how the company evaluates technical and business capabilities.

Coding & SQL

These questions assess your ability to manipulate data efficiently and write clean, production-ready code.

• Write a SQL query to calculate the rolling 7-day active user count for Peacock subscribers.
• Given a dataset of user streaming sessions, write a Python function to find the top three most-watched genres per user.

Preparing for an interview at NBCUniversal requires a balanced approach that covers technical execution, structured problem-solving, and communication.

Role-Related Knowledge – You must demonstrate a strong command of SQL, Python, and core machine learning algorithms. Be prepared to write clean code on a shared screen and explain the mathematical foundations of the models you use.

Problem-Solving & Business Acumen – Technical skills are only valuable if they solve the right business problems. You must show that you understand media-specific metrics like Customer Lifetime Value (CLV), churn, and user engagement, and can translate business goals into data science frameworks.

Communication & Influence – NBCUniversal highly values collaboration. You need to prove that you can partner effectively with product managers, engineers, and executive stakeholders, translating technical complexity into clear, actionable business recommendations.

The interview process for a Data Scientist at NBCUniversal typically takes between 2 to 3 months, depending on the team alignment and hiring manager availability. The company places a strong emphasis on finding the right team match, which can sometimes extend the overall timeline. The process is designed to thoroughly evaluate your technical capabilities, problem-solving structure, and behavioral alignment.

Your journey begins with an initial application and ranking of preferred teams, followed by an automated screening phase. The subsequent stages involve direct interaction with data science leaders, peer team members, and executive leadership to ensure a comprehensive evaluation.

The typical progression of the interview stages includes:

• Recruiter Screening Call (20 minutes): A brief introductory call to discuss your background, interest in NBCUniversal, and basic alignment with open roles.
• HireVue AI / Virtual Interview: A behavioral screening consisting of pre-recorded video questions. This round serves as a critical filter, so clear communication and structured answers are essential.
• Technical Round (1 hour): Conducted by a Lead or Senior Data Scientist. This includes live Python and SQL coding (30 minutes), a resume deep dive focusing on your ML experience (15 minutes), and a business case study (15 minutes).
• Hiring Manager & Team Interviews: One or two video calls with the hiring manager and team members. These sessions combine technical deep dives, past project discussions, and behavioral scenarios.
• VP / Leadership Interview: A final conversation with a Vice President or Director, focusing on high-level system design, coding experience, and strategic alignment with the business.

This visual timeline illustrates the typical path from your initial application to the final offer. Candidates should use this sequence to pace their preparation, ensuring they master behavioral framing for the early HireVue stage before pivoting to deep technical and case study prep for the subsequent rounds. Note that while some teams may introduce a take-home assignment or an ML system design session in the later stages, the core progression remains consistent.

SQL & Python Live Coding

The live coding assessment at NBCUniversal is designed to test your practical, hands-on programming skills. Interviewers want to see if you can write efficient, bug-free code under time constraints. The difficulty typically ranges from easy to medium, making strong foundational knowledge and speed key differentiators.

Be ready to go over:

• SQL Joins and Aggregations – Mastering complex joins, window functions (like ROW_NUMBER() and LAG()), and group-by operations on transactional user data.
• Data Manipulation in Python – Efficiently using libraries like Pandas and NumPy to clean, filter, and transform datasets.
• Algorithm Basics – Standard data structures (lists, dictionaries, sets) and writing basic logic loops to solve data processing tasks.
• Advanced concepts (less common) – Optimizing query execution plans, handling extremely large datasets (PySpark), and writing custom generator functions in Python.

Example questions or scenarios:

• "Write a SQL query to find the top 5 most popular movies on Peacock for each day of the past week."
• "Create a Python function that takes a list of timestamps and groups them into active sessions based on a 30-minute inactivity threshold."

Machine Learning & Statistical Modeling

This evaluation area tests your theoretical knowledge of machine learning and your ability to apply it to real-world datasets. You will be asked to explain the "why" behind your modeling choices, including algorithm selection, feature engineering, and validation strategies.

Be ready to go over:

• Supervised Learning Foundations – Deep understanding of regression, classification, decision trees, and ensemble methods (Random Forests, XGBoost).
• Model Evaluation Metrics – Choosing the right metrics (ROC-AUC, Precision-Recall, F1-score) based on business constraints like class imbalance.
• Modern AI Architectures – Understanding newer architectures, such as Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs), and how they can be applied to media data.
• Advanced concepts (less common) – Multi-task learning, deep learning for recommendation systems, and causal inference modeling.

Example questions or scenarios:

• "How would you build a model to predict user churn, and how would you handle the fact that only 2% of users churn in any given month?"
• "Explain how you would implement a RAG system to help customer service agents quickly find answers in our internal programming documentation."

Business Case Studies & Metrics

The case study round is a critical differentiator at NBCUniversal. It tests your business acumen and your ability to translate vague business goals into structured data science problems. You must show that you understand how a media business operates and how to measure success.

Be ready to go over:

• Customer Lifetime Value (CLV) – Modeling and predicting the long-term monetary value of a customer.
• Cohort Analysis – Tracking and comparing user behavior across different signup groups or promotional campaigns.
• A/B Testing & Experimentation – Designing statistically sound experiments, determining sample sizes, and interpreting results.
• Advanced concepts (less common) – Media mix modeling (MMM), multi-touch attribution, and price elasticity modeling for subscription tiers.

Example questions or scenarios:

• "How would you design a framework to evaluate whether acquiring a new sports broadcasting license will be profitable for Peacock in the long run?"
• "If we launch a new user interface for the Peacock app, how would you set up an experiment to prove it increases user engagement?"

ML System Design

For mid-to-senior roles, you will face system design questions that evaluate your ability to architect scalable, reliable, and maintainable machine learning systems in production.

Be ready to go over:

• Data Pipelines – Designing efficient ETL processes for streaming and batch data.
• Model Deployment & Monitoring – Strategies for deploying models (APIs, batch offline inference) and monitoring them for data drift.
• Scalability – Architecting systems that can handle real-time requests from millions of active users.
• Advanced concepts (less common) – Feature stores, online learning systems, and distributed training setups.

Example questions or scenarios:

• "Design an end-to-end recommendation system for Peacock that suggests content to users on the homepage in real-time."
• "How would you architect a system to detect fraudulent account sharing across different geographical locations?"