Senior Machine Learning Scientist, Creative Generation & Personalization

About the role

The team develops advanced research and applies it to solve complex advertising problems at streaming scale. Results from this work integrate into production systems that enable real-time engagement with streaming audiences.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Analysis of auction dynamics, control systems, and reinforcement learning informs the design of experimentation frameworks. This analysis shapes A/B and multivariate testing approaches used by the Advertising Performance group.

Production systems operating at TV streaming scale consume inputs from research and development.

Requirements

The posting states a pay range of $148750 to $361000.

A PhD in computer science, statistics, applied math, or a related quantitative discipline is mandatory to meet the scientific depth required for this role.

At least 8 years of applied research experience using statistical and deep learning techniques is required to address real-world advertising problems.

Peer-reviewed publications on deep learning models for advertising or related domains must demonstrate original contribution and technical rigor to the field.

Effective communication and collaboration with cross-functional teams must function well in a fast-paced, action-oriented setting. Clear explanation of methods and alignment with product goals are essential for success.

Practical notes

This position is based in San Jose and follows a hybrid model, generally working in the office Monday through Thursday with flexibility for remote work on Fridays.

Reasonable accommodations are provided in accordance with applicable law; please direct inquiries to EmployeeRelations@Roku.com.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Machine learning practitioners in streaming media use discriminative and generative deep learning models to predict user behavior and optimize content delivery.

Reinforcement learning and auction dynamics are common tools for realtime decision-making in large-scale advertising systems.

Research in advertising technology often requires balancing statistical rigor with fast deployment to production.

Streaming platforms rely on forecasting and time series models to manage content supply and demand across audiences.

Collaboration across product, engineering, and data science teams is essential for turning research into user-facing features.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.