Senior Data Scientist

Roku•New York, NY

4h•$157,000 - $187,000•Hybrid

About The Position

Roku is looking for a Senior Data Scientist to join the Core Analytics team and drive data-driven insights for our Customer Intelligence function, part of the broader Planning & Optimization team. Together, we answer high-impact business questions from senior leaders with clear, decision-ready insights. You’ll work on customer analytics that directly shape how Roku grows engagement, retention and monetization across the streaming ecosystem, with visibility to cross-functional leaders and meaningful ownership over high-impact questions. This role is designed to operate at the intersection of analytics and business strategy. We're looking for someone who can translate customer behavior into metrics and narratives which can help define our customer strategy, and/or measure the impact of initiatives on customer behavior. You’ll be expected to translate ambiguous stakeholder needs into strong analytical narratives and executive-ready deliverables. A core part of this work involves measuring incremental impact and distinguishing true effects from noise. Familiarity with causal inference and experimentation is important; deep specialization is a plus but not required.

Requirements

5+ years of experience in data science or advanced analytics roles driving measurable business impact.
Demonstrated ownership of ambiguous, high-visibility analytical problems.
Experience building reusable measurement frameworks or scalable impact evaluation methodologies.
Strong proficiency in Excel, SQL and Python for data analysis and visualization.
Experience building reusable analytical code and productionizing workflows (e.g., Airflow or similar tools).
Hands-on experience with Tableau or Looker for reporting.
Ability to communicate complex findings clearly to both technical and executive audiences.

Responsibilities

End-to-end Advanced Analytics: lead the design, execution, and delivery of complex analyses, leveraging advanced analytics to understand customer behavior, predict churn, identify growth opportunities, and measure the incrementally of initiatives.
Technical Execution & Automation: develop, optimize, and maintain scalable SQL and Python-based data pipelines and analytical workflows.
Write production-quality code to create datasets, compute KPIs, validate results, and build predictive/causal models when needed.
Partner with Data Engineering to enhance data quality, metric consistency, and pipeline reliability, including leveraging tools like Airflow.
Dashboarding & Reporting: create and maintain Tableau/Looker dashboards and recurring scorecards that empower business teams with self-service insights, reducing ad-hoc requests and enabling continuous performance monitoring.
Raise Analytical Bar: champion best practices in statistical rigor, bias awareness, and data governance. Continuously explore new methodologies and tools to elevate the team's analytical capabilities.
Business-Oriented Storytelling & Communication: craft compelling, executive-ready narratives and presentations (slides, memos) that clearly articulate complex analytical findings, strategic implications, and actionable recommendations to both technical and non-technical audiences, influencing decision-makers across the organization.
Stakeholder Partnership: clarify objectives, assumptions, tradeoffs, and decision points; ensure outputs are usable.