Principal Product Manager (ML / Subscriptions Growth)

Requirements

• You bring 8+ years of product management experience, including leading data-driven or ML-backed products from discovery through global rollout.
• You have led platform or shared-capability product work where driving alignment and adoption was key to success, not just shipping features.
• You have strong technical fluency in experimentation design, A/B testing, causal inference concepts, ML fundamentals, recommender/personalization systems, and data infrastructure (feature stores, attribute systems, data pipelines, data governance).
• You have experience defining and governing shared data products or data layers - establishing standards and processes.
• You consistently connect product decisions to measurable business outcomes, with a track record of improving conversion, retention, churn, ARPU, and/or LTV.
• You have experience in subscription or e-commerce business models, including lifecycle optimization, offers, pricing, packaging, or marketplace dynamics.
• You demonstrate strong judgment in choosing the simplest effective solution, and you know when advanced ML or GenAI is worth the trade-offs.
• You resist over-engineering and insist on proving incremental value.
• You build responsibly, considering fairness, bias mitigation, privacy, and localization — especially when personalization affects pricing, offers, or eligibility.
• You lead with clarity and empathy, give and receive feedback well, and raise the bar through coaching and principled decision-making.
• You influence without authority across engineering, data science, design, analytics, finance, and commercial stakeholders, and you’re skilled at aligning teams through ambiguity.
• You are experienced with resolving trade-offs between competing priorities.

What the job involves

• 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.
• 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.