Research Scientist, Recommendation Systems

📋 Description

• • Hook is at the forefront of revolutionizing music discovery and fan engagement, building the next generation of how people interact with music. As a Research Scientist specializing in Recommendation Systems, you will play a pivotal role in shaping the core of our user experience. You will be instrumental in designing, developing, and advancing state-of-the-art recommendation systems that are crucial for delivering personalized content and experiences to our rapidly growing user base at scale. This is a unique opportunity to conduct cutting-edge applied research, translate complex theoretical insights into robust, production-ready machine learning models, and collaborate closely with our talented engineering and product teams to drive tangible, measurable impact across the platform.
• • Your work will directly address some of the most challenging and exciting problems in the field of recommendation systems. This includes, but is not limited to, optimizing ranking algorithms to surface the most relevant content, developing sophisticated user modeling techniques to understand individual preferences and behaviors, implementing advanced exploration-exploitation strategies to balance novelty with relevance, and leveraging representation learning to create rich, informative embeddings for users and music items. You will be working with rich, real-world datasets that capture the dynamic nature of music consumption and creation.
• • Key responsibilities will involve the end-to-end lifecycle of machine learning models. You will be responsible for designing, implementing, and rigorously evaluating these models, focusing on recommendation, ranking, and personalization tasks. This includes developing novel approaches and refining existing ones to enhance model quality, ensure robustness under various conditions, and optimize for computational efficiency. A significant part of your role will be to develop and execute comprehensive experimentation strategies, utilizing both offline evaluation metrics to assess model performance theoretically and online A/B testing to measure real-world impact on user engagement and satisfaction.
• • You will delve into complex, large-scale datasets to gain deep insights into user behavior patterns, identify key drivers of engagement, and understand the performance characteristics of our recommendation systems. This analytical rigor will inform your model development and strategic decision-making. Furthermore, you will be a key collaborator, working hand-in-hand with our engineering and product teams. Your expertise will be crucial in the seamless transition of research prototypes into production-ready systems, ensuring that our innovations are effectively deployed and scaled to benefit millions of users.
• • Effective communication is paramount. You will be expected to share your research findings, methodologies, and results in clear, concise, and accessible ways. This includes presenting to both highly technical audiences, such as fellow researchers and engineers, and non-technical stakeholders, including product managers and leadership, enabling informed decision-making across the company. Continuous learning is also essential; you will be expected to stay abreast of the latest advancements in recommendation systems, machine learning, and related methodologies, actively seeking opportunities to apply new knowledge to our challenges.
• • This role offers an unparalleled opportunity to influence Hook's research direction. You will be instrumental in defining how we develop novel models, push the boundaries of technical innovation, and successfully bring cutting-edge scientific advancements into our production environment. You will have significant autonomy and ownership, with the chance to build and shape a world-class applied research function from its foundational stages. Your contributions will directly impact the growth and success of a fast-paced, innovative consumer technology company.
• • The ideal candidate will possess a strong theoretical understanding coupled with practical experience in building and deploying machine learning models. You should be comfortable navigating ambiguity, tackling complex problems with creative solutions, and thriving in a collaborative, fast-paced startup environment. Your passion for music and understanding of its social and creative dimensions will be a significant asset in this role, enabling you to build systems that truly resonate with our users and creators.
• • Ultimately, this position is for a driven and innovative Research Scientist who is eager to make a substantial impact on a platform that is redefining the music industry. You will be empowered to explore new frontiers in AI and machine learning, directly contributing to a product that fosters creativity, connection, and discovery within the global music community.