Group Product Manager

Job Description:

Rakuten International is a division of Rakuten Group, Inc., a Japanese global technology leader in services that empower individuals, communities, businesses and society. Headquartered in San Mateo, California with more than 4,000 employees worldwide, the Rakuten International business portfolio includes market leaders in e-commerce, digital marketing, advertising, communications and entertainment. We create products and services that provide exceptional value by aligning members and the businesses that want to engage them in a shared community. 

Rakuten is the most rewarding way to shop, giving millions of members Cash Back when they buy from their favorite brands. As a leading shopping platform, Rakuten partners with thousands of top brands across apparel, beauty and wellness, grocery, travel, on-demand services, subscriptions, and dining, helping members save on everyday purchases. Since 1999, Rakuten members have earned more than $4.6 billion in Cash Back, making it the largest Cash Back platform of its kind. Learn more at Rakuten.com.


Job Summary:

Rakuten Rewards is building the measurement infrastructure that defines how we prove — and grow — our value with merchants. We are looking for a Group Product Manager to own the Merchant Measurement product agenda end-to-end: standardizing our impressions data foundation, validating our performance through third-party benchmarking and shadow studies, productizing incrementality testing at scale, and delivering a self-serve merchant data hub.

This is both a hands-on and a people-leadership role. You will be the subject matter authority on merchant measurement in every room — with engineers, data scientists, merchants, and senior leadership — while also building and developing a small team of product managers who execute against the roadmap. You will report to the SVP of Product Management and partner closely with engineering, data science, commercial, and legal.

Key Responsibilities:
 

• Own the product strategy for Rakuten Rewards' data infrastructure — the systems that capture, validate, and route behavioral and transactional signals across member, merchant, and partner surfaces in real time.
• Own the product for standardizing Rakuten's impressions and merchant measurement products —establishing it as the single source of truth for all downstream measurement, testing, and merchant-facing reporting.
• Lead the development of unified member and merchant profiles — rich, continuously refreshed representations of behavior, preferences, intent, and performance that serve as the intelligence layer powering personalization, recommendations, and targeting across the platform.
• Lead the strategy, roadmap, and delivery of Rakuten Rewards' ML platform — the foundational infrastructure that enables data science and engineering teams to build, experiment with, deploy, and monitor machine learning models at scale.
• Enable incrementality testing at near-scale across top merchants and Lead Rakuten's engagement with third-party measurement platforms to benchmark performance
• Define and deliver a self-serve, cloud-agnostic merchant data hub that gives merchants direct access to their performance data, with scalable data governance and permissioning developed in partnership with Commercial and Legal.

Cross-Functional Leadership and Team Development

• Serve as the internal authority on Data and AI product strategy — across executive reviews, merchant conversations, cross-functional planning, and external evaluations.
• Drive alignment across engineering, data science, commercial, legal, and operations — proactively surfacing open questions and unblocking teams before issues compound.

Qualifications:

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Data and AI Product Depth

• Deep understanding of the modern data stack — eventing systems, data pipelines, feature engineering, and how data flows from raw capture to AI-ready form.
• Hands-on experience defining and shipping AI or ML platform capabilities: experiment management, model deployment infrastructure, feature stores, or monitoring frameworks.
• Experience with graph databases or graph-based data models, and an understanding of ho