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Description
Own the strategy and execution plan to scale curation through models, agents, and automation — sequencing workstreams by impact and driving them from concept to production. Partner with Engineering and Data Science to develop agents that take on day-to-day curation work, from defining requirements through evaluation and production rollout. Help establish the key infrastructure to support, integrate, and orchestrate agents over time — shaping how agentic systems plug into existing curation workflows and data. Define the future operating model for curation — determining where agents handle work autonomously and where humans stay in the loop for high-priority, high-risk, or quality-assessment cases, including review of agent output. Translate operational challenges into clear, actionable specifications that engineering and data science teams can execute against. Break down ambiguous problems into well-scoped, prioritized workstreams, sequenced by impact. Start with the highest-leverage slice; iterate from there. Evaluate technical proposals and model outputs — participate in design reviews and build eval frameworks (golden sets, quality metrics, error analysis, human-in-the-loop feedback) to judge whether AI/ML and agentic solutions are ready for production. Design and iterate on human-in-the-loop tooling that surfaces agent and model outputs (confidence scores, candidate matches, suggested labels, risk signals) so reviewers can validate high-stakes decisions with speed and accuracy. Run ROI and impact analyses to distinguish short-term tactical wins from long-term structural solutions, and to prioritize where automation delivers the most leverage. Communicate upward — build clear, compelling narratives and decks, and present progress, tradeoffs, and recommendations to senior leadership. Partner cross-functionally with Engineering, Data Science, Product, Editorial, Analytics, and peer Operations teams (including Artist Curation, Shazam Operations, Partner Operations, and Data Strategy) to surface interdependencies and align priorities.
Minimum Qualifications
5+ years in operations, program management, or technical product roles with meaningful exposure to AI/ML-driven workflows. Demonstrated experience translating operational or product problems into ML requirements and working directly with data scientists and engineers through model development cycles. Exposure to LLM-based workflows, prompt evaluation, or agentic automation — particularly applied to content moderation, classification, or operational workflows. Conceptual familiarity with content classification and content-risk tradeoffs, sufficient to judge where human review is warranted. Demonstrated track record of managing multiple concurrent workstreams with consistent on-time delivery and proactive stakeholder communication. Experience evaluating model outputs — designing eval sets, quality metrics, error analysis, and human feedback loops. Experience designing or specifying review/annotation tooling that surfaces AI inputs to human reviewers. Conceptual understanding of core analytics principles (sampling, hypothesis testing, statistical measurement). Proven ability to produce executive-ready written narratives and presentations. Track record of leading ambiguous initiatives end-to-end with minimal direction.
Preferred Qualifications
Experience in the music industry or with music metadata, catalogs, rights data, genre taxonomies, or editorial/streaming products. Hands-on experience building or deploying agentic systems in production. Experience collaborating with international stakeholders. Comfort querying data (SQL or similar) to investigate catalog issues independently. Tableau or comparable BI tooling. A genuine passion for music and curiosity about the systems that power the modern music industry. Bachelor's degree in Engineering, Business/Economics, or equivalent experience.
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