Senior Product Manager, Recommendations

Roku•New York, NY

3d•$195,000 - $303,000•Hybrid

About The Position

Roku is seeking a Senior Product Manager to lead critical product areas within the Recommendations team. This team is responsible for powering nearly all Roku-controlled UI surfaces, personalizing content, apps, and offers across various browse pages, carousels, rows, and destination experiences. The system aims to match over 100 million viewers with millions of entertainment entities daily, focusing on user engagement, monetization, and long-term platform health. In this high-ownership, high-visibility role, you will define product strategy, lead experiments, and deliver measurable outcomes that enhance viewer engagement KPIs and business model results across ad-supported, subscription, and transactional video on demand (TVOD/PVOD) models, leveraging the ML/AI recommendations ecosystem. You will collaborate closely with engineering, UX, data science, content, business, and cross-functional PMs to shape the future of Roku's recommendations platform.

Requirements

7+ years of product management experience, ideally in recommendations, personalization, search/discovery, or ML/AI-driven product areas.
Strong analytical rigor: comfort with experiment design, A/B testing, statistical significance, and metric frameworks. Ability to define, defend, and prevent drift on KPIs.
Proven ownership and bias to action: track record of driving complex, ambiguous initiatives end-to-end — from problem framing through strategy to shipped outcomes. You escalate early, propose alternatives, and don't let work stall.
Technical depth: experience working closely with ML engineers and data scientists on ranking, optimization, and signal-based systems. Ability to engage meaningfully in technical discussions about model performance, feature engineering, and system architecture.
Excellent cross-functional leadership: demonstrated ability to influence and drive alignment across engineering, data science, design, business, and content teams without direct authority.
Outcome-oriented experimentation mindset: experience designing and running experiments that drive real product and business decisions.
Clear, concise communicator: strong written and verbal skills; ability to present strategy, trade-offs, and recommendations to executive leadership.
BS/MS in a quantitative field (engineering, CS, mathematics); MBA a plus.

Nice To Haves

Experience in streaming, media, entertainment, or large-scale content platforms.
Familiarity with recommendation systems, content ranking, collaborative filtering, or personalization at scale (100M+ users).
Experience with GenAI/LLM applications in product discovery, content understanding, or signal generation.
Experience operating across multiple product surfaces (TV, mobile, web) with different user behaviors and constraints.

Responsibilities

Define and own the product strategy and roadmap for your area within Platform Recommendations, with a focus on maximizing user engagement and business value across all monetization models.
Identify unique problem spaces across surfaces and develop strategic recommendations backed by data and analysis.
Drive material outcomes from strategic themes through deep, independent analysis, quantified impact, coherent recommendations, and leadership-ready proposals.
Articulate customer and business problems clearly, evaluate options and trade-offs, and develop sequenced roadmaps that move the ball forward strategically.
Lead KPI definition and defense: propose clear primary decision metrics and supporting metrics for every initiative, especially in ambiguous domains where north-star KPIs are not easily defined.
Own experiment design end-to-end: define test contracts and hold the line on experimental rigor.
Drive experiments to decisions: ensure every test is tied to a concrete decision or roadmap change, and prevent KPI drift by proactively aligning stakeholders.
Move fast and demonstrate extreme ownership of outcomes: actively track, unblock, and push for value, and propose concrete options when faced with trade-offs.
Maintain 100% lockstep with partners in ML Research and Engineering, Merchandising, and Analytics with detail orientation and ensure alignment on strategy, outcomes and requirements/timelines.
Communicate with clarity and conciseness, with high signal-to-noise in written documents, messaging, presentations and verbal discussions.
Proactively keep stakeholders informed of progress, risks, and blockers with specific impacts, options and recommended paths.