Preparing for an interview at Roku requires a balanced approach that covers technical execution, statistical theory, and business strategy. You must demonstrate not only that you can write flawless code but also that you understand the business implications of your analytical choices.

SQL and Data Manipulation Proficiency – You must be able to write complex queries quickly and accurately. Expect to be tested on window functions, self-joins, aggregations, and CTEs. Your interviewers will look for clean, readable code and an understanding of query performance and optimization.

Product Intuition & Experimentation Rigor – Roku is a highly data-driven company that relies heavily on A/B testing. You need to demonstrate a deep understanding of experimental design, metric definition, and how to resolve conflicting metrics. Be prepared to talk about how user behavior translates into business value.

Statistical and Analytical Depth – You should have a strong grasp of foundational statistics, including regression modeling, hypothesis testing, and probability. You need to be able to justify your methodological choices and explain complex mathematical concepts in simple terms to non-technical stakeholders.

Cross-Functional Communication & Influence – Data scientists at Roku work closely with Product Managers, Data Engineers, and Finance teams. You must demonstrate that you can collaborate effectively, manage stakeholder expectations, and translate raw data insights into compelling business narratives.

The interview process for a Data Scientist at Roku is rigorous and thorough, often spanning multiple rounds to evaluate both your technical capabilities and your cross-functional communication skills. Candidates frequently report that the process can take anywhere from three to seven stages, depending on the specific team, location, and seniority of the role.

The journey typically begins with an initial recruiter screening to discuss your background, career goals, and alignment with Roku's culture. This is quickly followed by a technical screening, which almost always features a live coding assessment centered on SQL. Candidates who advance past this stage will enter a series of deep-dive interviews. These rounds focus on statistical theory, product case studies, and behavioral fit, culminating in conversations with hiring managers and cross-functional stakeholders such as product managers and data engineers.

The visual timeline above outlines the typical progression of the Roku data science interview loop. Candidates should use this sequence to pace their preparation, focusing heavily on core technical skills in the early stages before transitioning to high-level product cases and behavioral preparation for the final rounds. Note that while some teams may consolidate certain steps, you should prepare for a comprehensive evaluation across all highlighted domains.

To succeed in the Roku data science interview loop, you must perform consistently well across several distinct evaluation areas. Below is a detailed breakdown of what to expect and how to demonstrate mastery in each domain.

SQL Live Coding (HackerRank)

The live coding assessment is a critical gateway in the Roku interview process. This round is designed to evaluate your ability to manipulate data efficiently and write production-grade queries under time constraints.

Be ready to go over:

• Complex Joins and CTEs – Combining multiple tables, utilizing self-joins, and structuring queries logically using Common Table Expressions.
• Window Functions – Running calculations across sets of rows related to the current row (e.g., RANK(), LEAD(), LAG(), SUM() OVER()).
• Data Aggregation and Filtering – Grouping data dynamically, handling null values, and writing precise conditional logic using CASE WHEN statements.
• Advanced concepts (less common) – Query optimization techniques, indexing strategies, and writing scalable queries for distributed data warehouses.

Example questions or scenarios:

• "Write a query to identify the top 3 most-watched content genres for each user over the past 30 days, ranking them by total duration."
• "Calculate the month-over-month retention rate of active streaming users, defining retention as streaming at least once in consecutive months."
• "Given a table of ad impressions and a table of user clicks, write a query to calculate the click-through rate (CTR) per ad campaign, handling potential division-by-zero errors."

Product Case Studies & Experimentation (A/B Testing)

This area evaluates your product sense and your ability to design scientific experiments that guide product development. Roku relies on these case studies to see if you can think like a business owner.

Be ready to go over:

• Experiment Design – Defining null and alternative hypotheses, calculating sample sizes, determining run times, and establishing guardrail metrics.
• Metric Frameworks – Selecting primary, secondary, and counter-metrics to measure the holistic impact of a product change.
• Metric Trade-offs - Analyzing scenarios where metrics move in opposite directions and determining the net impact on the business.

Example questions or scenarios:

• "We are testing a new recommendation layout that increases click-through rates on premium content but decreases overall time spent on the platform. How do you evaluate this trade-off?"
• "Design an A/B test to evaluate a new auto-play feature for trailers on the home screen. What are your primary success metrics, and what risks would you monitor?"
• "How would you detect and account for network effects or user interference in a shared streaming environment?"

Statistical Foundations & Modeling

This section tests your theoretical knowledge of statistics and your ability to apply quantitative methods to real-world business problems.

Be ready to go over:

• Hypothesis Testing – Understanding p-values, Type I/II errors, statistical power, and confidence intervals.
• Regression Analysis – Practical application of linear and logistic regression, including model assumptions, diagnostics, and interpretation of coefficients.
• Correlation vs. Causation – Identifying confounding variables and understanding techniques to establish causal relationships in observational data.

Example questions or scenarios:

• "Explain the difference between L1 (Lasso) and L2 (Ridge) regularization, and when you would choose one over the other."
• "What happens to your statistical power if you increase the sample size of your control group but keep the treatment group size constant?"
• "How would you handle a dataset with highly imbalanced classes when training a model to predict user churn?"

Stakeholder Management & Collaboration

Because data scientists at Roku act as advisors to product and business leaders, your ability to communicate complex technical findings to non-technical audiences is highly valued.

Be ready to go over:

• Translating Data to Insights – Presenting analytical findings as clear, actionable business recommendations.
• Conflict Resolution – Navigating situations where data insights contradict a stakeholder's intuition or product roadmap.
• Prioritization – Managing competing demands from engineering, product, and finance teams.

Example questions or scenarios:

• "Describe a time when your analytical recommendations were met with skepticism by a product manager. How did you handle the situation?"
• "How do you explain the concept of statistical significance to a business stakeholder who wants to launch a feature before the experiment has reached full power?"
• "Walk me through how you would collaborate with a data engineering team to define the logging requirements for a new product feature."

As a Data Scientist at Roku, your day-to-day responsibilities will center on driving product innovation and operational excellence through rigorous data analysis. You will be responsible for translating complex user behaviors into clear business strategies, ensuring that the platform continues to deliver highly personalized experiences to millions of viewers.

You will spend a significant portion of your time collaborating with cross-functional partners. This includes working closely with Product Managers to define key performance indicators (KPIs) for new features, collaborating with Data Engineers to design scalable data pipelines, and partnering with Engineering teams to deploy machine learning models. You will be the analytical anchor for your product group, ensuring that all product decisions are backed by statistical evidence.

Typical projects in this role include:

• Designing and analyzing complex A/B tests to optimize user engagement, content recommendations, and ad-delivery algorithms.
• Building predictive models to forecast user churn, lifetime value (LTV), and content demand.
• Developing automated dashboards and reporting frameworks to monitor product health and identify emerging trends.
• Conducting deep-dive exploratory analyses to uncover friction points in the user journey and recommend product improvements.

To be competitive for a Data Scientist role at Roku, you must possess a strong blend of technical expertise, analytical rigor, and communication skills. The ideal candidate has a proven track record of delivering business impact through data-driven insights.

• Must-have technical skills – Advanced proficiency in SQL (window functions, CTEs, complex joins) and programming expertise in Python or R for data analysis and machine learning.
• Experience level – Typically 5+ years of professional experience in a data science or quantitative analytics role, preferably within tech, streaming, or digital media.
• Academic background – A Bachelor’s, Master’s, or Ph.D. in a highly quantitative field such as Statistics, Mathematics, Computer Science, Economics, or Engineering.
• Core competencies – Deep understanding of experimental design (A/B testing), statistical modeling, and machine learning algorithms.
• Soft skills – Exceptional communication skills with a demonstrated ability to influence cross-functional stakeholders and translate complex technical concepts into actionable business strategies.

Q: How technical is the recruiter screening at Roku? A: Candidates are often surprised by the technical nature of the initial recruiter screening. Be prepared for the recruiter to ask high-level technical questions regarding your experience with SQL, Python, and statistical modeling, in addition to standard behavioral questions.

Q: What is the coding environment like during the SQL technical interview? A: The technical screening is typically conducted via HackerRank or a similar live-coding platform. You will be expected to write clean, executable SQL queries to solve problems of varying difficulty. Depending on the interviewer, you may not be allowed to run exploratory queries, so writing structured, accurate code on your first attempt is highly valued.

Q: How does Roku evaluate culture fit? A: Roku values candidates who are collaborative, self-motivated, and highly focused on delivering measurable business impact. They look for professionals who can navigate ambiguity, communicate clearly with non-technical partners, and take extreme ownership of their analytical projects.

Q: What is the typical timeline from the first screen to an offer? A: The timeline can vary significantly. While some candidates complete the process in approximately three weeks, others report a longer timeline of six to eight weeks, particularly if the role involves multiple specialized rounds or stakeholder interviews across different time zones.

To maximize your chances of success during the Roku data science interview loop, consider these practical, insider tips:

• Confirm interview logistics early – Candidates have occasionally reported coordination issues, such as missing video links or conflicting timezone invitations. Double-check your interview confirmations and proactively reach out to your recruiter if any details are unclear.

• Prepare for multiple SQL evaluations – Because Roku has multiple distinct teams, you may encounter similar technical coding screens in different rounds. Approach every coding challenge with the same level of focus and professionalism, even if the questions feel familiar.
• Structure your case study answers – When presented with vague product scenarios, do not jump straight to a solution. Use a structured framework: clarify the business goal, define the target user, propose your metrics, outline your experimentation strategy, and discuss potential risks.
• Be ready to explain the 'Why' behind your resume – Your interviewers will ask you to walk through projects listed on your resume. Be prepared to explain not just what you built, but why you chose that specific methodology, how you validated your results, and what the ultimate business outcome was.

Securing a Data Scientist role at Roku is an exciting opportunity to work at the forefront of the streaming entertainment industry. The role offers the chance to tackle massive scale, solve complex personalization challenges, and directly influence the media consumption habits of millions of global users. By demonstrating deep technical mastery, structured product thinking, and strong cross-functional communication, you can stand out as an exceptional candidate.

As you prepare, focus your efforts on mastering advanced SQL queries, refining your A/B testing frameworks, and practicing how you communicate technical concepts to non-technical partners. Remember that Roku is looking for strategic thinkers who can connect data to business value. For more real-world interview insights, detailed company guides, and practice questions, you can explore additional resources on Dataford.

The compensation data above represents the typical salary range for a Senior Data Scientist at Roku in San Jose, CA. This wide range reflects the company's commitment to attracting top-tier talent and accommodates variations in experience, specialization, and interview performance. Candidates should use this data to understand the competitive landscape and guide their compensation expectations during the offer stage.