
Product Manager - Lifecycle
Redwood City, CAFull-timePosted 4mo agoStill listed 4 days ago
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Job overview
Poshmark is hiring a Product Manager - Lifecycle. Poshmark seeks a Product Manager to own lifecycle channel strategy and execution across email, push notifications, promotional placements, and CRM touchpoints, partnering with engineering, data, and merchandising teams to drive retention, conversion, and engagement for buyers and sellers.
Key focus areas include Own end-to-end strategy and roadmap for lifecycle channels across buyer, seller, and merchandising audiences, Translate martech platform capabilities into channel-level features and experiments, and Define and optimize message surfaces ensuring coherent, personalized, conversion-driven experiences.
Successful candidates bring 4+ Years In Product Management Or Growth Role, Hands-On Experience With Customer Engagement Platform, and Experience Integrating Composable CDP. Important skills include MoEngage, Braze, Iterable, Salesforce Marketing Cloud, Hightouch, and Segment. Preferred (not required): Clear Point Of View, Document Work Rigorously, Accountability To Channel Performance, and AI Tools.
Skills & qualifications
Skills
Qualifications
Full job description
ABOUT POSHMARK
Poshmark is the leading fashion marketplace where style comes alive through discovery, self-expression, and human connection. Powered by a vibrant community of 165 million members, Poshmark brings real people and taste to shopping through a social experience shaped by shared discovery. Buying and selling fashion feels simple, joyful, and personal, while every item tells its own story. Poshmark empowers sellers to grow meaningful businesses, keeps fashion in circulation longer, and gives shoppers access to unique and trusted finds, from everyday pieces to one-of-a-kind vintage and luxury.
Own how Poshmark shows up in users' inboxes, notification trays, and feeds. This role is responsible for the channel strategy and surface execution across email, push notifications, promotional placements, and CRM touchpoints
You'll partner closely with engineering to translate platform capabilities into channel experiences that drive retention, conversion, and engagement across Poshmark's buyer, seller, and merchandising audiences. The expectation is that you show up with a clear point of view, document your work rigorously, and hold yourself accountable to channel performance, not just feature delivery.
Poshmark is also in the middle of an AI transformation that is changing how product development works. PMs on this team use AI tools actively to prototype, build, and ship, and this role is no exception.
Responsibilities:
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Own the end-to-end strategy and roadmap for Poshmark's lifecycle channels including email, push notifications, promotional placements, and CRM surfaces across buyer, seller, and merchandising audiences
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Translate martech platform capabilities into channel-level features and experiments
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Define and optimize the message surfaces across Poshmark's properties, ensuring channel experiences are coherent, personalized, and conversion-driven
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Work with data and analytics to build segmentation strategies, measure channel performance, and identify growth opportunities across the funnel
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Define and maintain channel health metrics, establishing baselines and owning improvement against them
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Own the product layer for promotional campaigns including discount mechanics, offer construction, audience targeting, and measurement
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Partner with merchandising and the seller team to design CRM-driven promotional programs that serve both sides of the marketplace
Required Qualifications
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4+ years in a product management or growth role
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Hands-on experience with a customer engagement platform (MoEngage, Braze, Iterable, Salesforce Marketing Cloud, or similar) at a product or implementation level, not just a user level
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Experience integrating a composable CDP (Hightouch, Segment, or similar) with a downstream engagement platform
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Background in email deliverability: domain reputation, list hygiene, ISP relationships, and inbox placement
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Prior work on AI-driven personalization systems, recommendation-based messaging, or predictive audience models
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Track record of running structured experiments across owned channels with proper methodology: experiment sizing, holdout groups, attribution, and incremental lift measurement
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Understanding of the difference between correlation and causation in lifecycle data, and how to design tests that actually answer the question you are asking
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