About the job
About Hook Music
Hook Music is an innovative social music platform, supported by prominent investors, that is pioneering the future of music discovery, fan engagement, and creator empowerment. We transform beloved songs into interactive sounds, allowing fans and creators to reimagine music through seamless, remix-friendly experiences that require no learning curve. By collaborating directly with artists, labels, and rights holders, Hook Music ensures that music is discovered, shared, and expressed in ways that honor and reward the creators behind it. At Hook, music transcends consumption; it is experienced, expressed, and personalized.
Role Overview
We are in search of a Research Scientist dedicated to designing, developing, and enhancing cutting-edge recommendation systems that facilitate personalized user experiences on a large scale. In this pivotal role, you will engage in applied research, translating theoretical insights into production-ready models, while collaborating closely with engineering and product teams to drive significant impact.
You will tackle challenges related to ranking, personalization, user modeling, exploration-exploitation, and representation learning, utilizing real-world data and user-generated content.
What You’ll Do
Design, implement, and evaluate advanced machine learning models for recommendation, ranking, and personalization
Conduct applied research to enhance model quality, robustness, and efficiency
Develop and implement experimentation strategies employing both offline evaluation and online testing
Analyze complex datasets to gain insights into user behavior and system performance
Collaborate with engineering and product teams to transition research innovations into production systems
Effectively communicate research findings in a clear and accessible manner to both technical and non-technical stakeholders
Stay updated on advancements in recommendation systems and relevant machine learning methodologies