About the job
Our Mission
Since its inception in 2012, Tinder® has transformed the dating landscape, achieving an astonishing milestone of over one billion matches within just two years. This rapid evolution underscores our commitment to fulfilling a fundamental human desire: authentic connection. With more than 630 million downloads and over 97 billion matches to date, Tinder serves around 50 million active users each month across 190 countries and 45+ languages, establishing a remarkable global presence. In 2024, Tinder proudly secured four Effie Awards for its inaugural global brand campaign, “It Starts with a Swipe”™.
Our Values
One Team, One Dream
We collaborate seamlessly, building Tinder for our community. Our success is rooted in cross-functional teamwork, innovative thinking, and a shared vision to achieve our mission.
Own It
We embrace accountability and strive to create a positive impact in every facet of our operations through ownership, innovation, and a dedication to excellence.
Never Stop Learning
We foster an environment where risk-taking is encouraged. We value feedback, celebrate achievements, and learn from failures to propel continuous improvement.
Spark Solutions
As problem solvers, we focus on proactive solutions, navigating challenges with agility to achieve our objectives without being hindered by past issues.
Embrace Our Differences
We intentionally cultivate a diverse workplace that mirrors our members' rich backgrounds. By leveraging varied perspectives, we enhance the experiences we offer to both our users and our team.
The Role
We are seeking a Senior Product Manager, Recommendations Cross-Surface Personalization to spearhead the connection of Tinder’s recommendation system with other product surfaces and teams.
The Recommendations system is pivotal in guiding what members see, when they see it, and the rationale behind it. Achieving true personalization necessitates collaboration not only within Recommendations but throughout the entire product ecosystem. In this role, you will facilitate the utilization of Recommendations data and insights across different teams. You'll also ensure the Recommendations team can efficiently manage and prioritize requests from various pods, establishing processes that enhance productivity and deliver effective solutions.
Your collaboration with cross-pillar teams and the Data Science, Machine Learning, and Recommendations Engineering departments will ensure that Recommendations data, models, and insights are consistently and effectively utilized across the platform.
