Principal Product Manager - ML / Subscriptions Growth

Spotify’s mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. The Subscriptions Mission builds and evolves Spotify’s subscription products and marketplace experiences to drive sustainable user and revenue growth globally. We focus on awareness, acquisition, activation, retention, and monetization strategies that help users unlock the full value of Spotify while enabling the business to scale efficiently and responsibly. User Understanding sits within Subscriptions and focuses on building the intelligence layer that makes our growth efforts smarter—through decisioning, data signals, and ML models that power personalization across surfaces and lifecycle moments. You’ll work at the intersection of machine learning innovation and commercial strategy, partnering closely with ML engineers, data scientists, data platform teams, product insights, and business stakeholders to shape high-impact growth initiatives. \n

What You'll Do Set the product vision and multi-quarter strategy for ML- and AI-driven subscriber growth across awareness, acquisition, activation, retention, and monetization. These strategies will define and inform how we develop data signal sourcing, data platform capabilities, and model validation. Own a portfolio of initiatives end-to-end, aligning multiple squads and stakeholders around clear goals, sequencing, and measurable outcomes (conversion, churn, ARPU, LTV). Turn behavioral and revenue signals into crisp hypotheses, define success metrics and guardrails, and drive an experimentation roadmap that’s prioritized by impact and learning value. Establish strong measurement practices (A/B testing, incrementality, causal inference where appropriate), and ensure we scale only what demonstrates durable lift against strong baselines. Set direction to prioritize and develop data signals, attributes, data products, and feature store requirements Drive lifecycle and personalization strategies (including trial design, offer targeting, win-back, and plan nudges) that balance user value, revenue outcomes, and long-term trust. Partner with pricing and packaging stakeholders to design experiments that improve monetization while remaining transparent, locally relevant, and sustainable. Make pragmatic decisions about when to use ML, GenAI, heuristics, or rules, balancing ROI, complexity, latency, maintainability, and launch risk. Start with simple solutions before investing in complex systems. Ensure production readiness for ML-backed experiences through monitoring, performance evaluation, guardrails, data quality standards, and rollout plans that work at global scale. Communicate trade-offs and insights clearly to senior leadership, translating technical complexity into business impact and crisp decisions. Elevate product craft across the mission by mentoring other PMs, strengthening decision frameworks, and building a culture of learning, accountability, and inclusive collaboration.