Senior Data Scientist
RokuAustin, TexasPosted 24 February 2026
Tech Stack
Job Description
Teamwork makes the stream work.
Roku is changing how the world watches TV
Roku is the #1 TV streaming platform in the U.S., Canada, and Mexico, and we've set our sights on powering every television in the world. Roku pioneered streaming to the TV. Our mission is to be the TV streaming platform that connects the entire TV ecosystem. We connect consumers to the content they love, enable content publishers to build and monetize large audiences, and provide advertisers unique capabilities to engage consumers.
From your first day at Roku, you'll make a valuable - and valued - contribution. We're a fast-growing public company where no one is a bystander. We offer you the opportunity to delight millions of TV streamers around the world while gaining meaningful experience across a variety of disciplines.
About the team
At Roku, as a Senior Data Scientists you will leverage Big Data to generate insights that fuel Roku’s most significant business decisions. As a Senior Data Scientist, you will also use a combination of technical skills, business intuition, and analytical thinking to partner with business leaders and provide actionable recommendations. Senior Data Scientists at Roku are thought partners to executive business leaders, so communication and presentation are enormously important. Senior Data Scientists do not take direction from business owners and execute ad-hoc questions. You will proactively identify the areas of the data and business that require attention and formulate analyses, insights, and recommendations based on an excellent command of the needs. The Senior Data Scientist role at Roku is a perfect fit for anyone who wants to continue to evolve business domain expertise alongside technical and analytical development.
About the role
Roku is looking for a Sr. Data scientist to join the Core Analytics team supporting the Operations team. As a Sr. Data Scientist, you will bring strong technical experience retrieving, manipulating, analyzing, and visualizing data. Just as importantly, you will bring excellent communication skills, proven collaboration track record, and business acumen.
The ideal candidate will have endless curiosity and can pair a global mindset with locally relevant execution. You should be a gritty problem solver and self-starter who can help drive initiatives with internal stakeholders. The successful candidate will display a balance of hard and soft skills, including the ability to respond quickly to changing business needs.
This role is hybrid and located in Austin, TX. Employees are expected to report to the office Monday - Thursday.
What you’ll be doing:
Develop and maintain dashboards, reports, and data visualizations to monitor key metrics for operational and systems health
Analyze structured and unstructured data and communicate insights to help stakeholders solve business problems, identify trends and make data-driven decisions.
Develop data visualizations that illustrate complex platform phenomena using Looker, Tableau etc.
Develop necessary data pipelines to power automation, validation and reporting.
Use creativity to find proxies where verbatim data or measurement is unavailable
Collaborate with stakeholders to align data science initiatives with organizational goals and strategy design and execute AB tests
Stay updated with the latest trends and technologies in data science to drive innovation
We’re excited if you have:
5+ years of relevant experience in data analytics, data science or business intelligence role. Previous experience in Logistics, operations, product analytics is preferred.
Excellent command of SQL and experience working with Big Data
Experience coding in Python or R.
Hands-on experience with data visualization platform like Tableau, Looker, or similar
Statistics knowledge: A/B testing, statistical inference, interpretation of data
Have a bias towards action in resolving issues and operate in a high-energy, fast-paced environment
Self-driven, highl ... (truncated, view full listing at source)
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