Senior or Staff Data Scientist, Recommendations

Suno•San Francisco, CA

22d

About The Position

As our founding Recommendation Data Scientist, you'll be instrumental in building Suno's music discovery and recommendation systems from the ground up. You'll help define how millions of users discover, create, and engage with music on our platform. This role combines technical expertise in recommendation systems with the creative challenge of applying strong principles and judgment to determine what truly compelling and valuable music recommendations look like. You'll work at the intersection of music, AI, and human behavior, collaborating closely with engineering, product, and growth teams to build systems that help users find their next favorite song or inspire their next creation. From hands-on data exploration and rapid prototyping to building up sophisticated ML models, you'll be deeply involved in the full spectrum of recommendation strategy and execution. This role is perfect for someone who thrives in open-ended environments, loves getting their hands dirty with data and prototyping, and is excited about defining the future of music discovery.

Requirements

6+ years experience in data science or machine learning roles with direct experience on recommendation systems - ideally in consumer products, music/audio, or content platforms
Strong technical skills in Python, SQL, and statistical modeling
Experience designing and running rigorous experiments, evaluating tradeoffs, deriving clear insights, and making actionable recommendations
Excellent communication and experience working across multiple functions to influence decisions
A self-starter mentality with the ability to thrive in ambiguity, eagerness to wear multiple hats, and passion for continuous learning

Responsibilities

Define content strategy: Partner with product and growth leaders to establish our initial content discovery strategy, recommendation goals, and success metrics
Get hands-on: Dive deep into user behavior patterns and content characteristics, using these insights to prototype and improve upon recommendation algorithms and features
Design and run experiments: Create rigorous testing frameworks to validate recommendation improvements and measure impact on user engagement, retention, and music creation
Build evaluation systems: Develop comprehensive frameworks for measuring recommendation quality across multiple dimensions - relevance, diversity, novelty, and user satisfaction
Collaborate cross-functionally: Work closely with engineering to test and implement recommendation algorithms and with product to shape user experience
Shape data culture: As a part of our growing data science team, contribute to strong data foundations and foster a nuanced and healthy company-wide relationship with data