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ABOUT THE TEAM AND WHAT WE'LL BUILD TOGETHER 

Kobie's Product Management organization is evolving from project execution to a P&L ownership model — where leaders drive revenue, customer satisfaction, and the commercial value of Kobie's data capabilities. This role sits at the center of that transformation.

As Director, Product you will define what Kobie's data platform means as a market-facing product: how behavioral and transactional data becomes the engine behind smarter loyalty programs, better marketing performance, and more personalized customer experiences. You will set strategy for KLICs and the Data Engine, build the commercial model to monetize them, and ensure our data capabilities fuel AI-driven outcomes — not just move data between systems. Success at year one looks like increased attach rates, at least one AI-powered capability live with measurable client outcomes, and clear differentiated positioning of Kobie's data platform in the market.

You will work directly with Product, Data Engineering, Client Services, and Commercial teams to convert Kobie's data assets into scalable, repeatable offerings.

HOW YOU WILL MAKE AN IMPACT

• Define Kobie's data product portfolio with a clear focus on marketing, loyalty, and customer experience outcomes — establishing product boundaries, value propositions, and positioning for KLICs and the Data Engine that go well beyond data movement or pipeline delivery.
• Set the standard for what "AI-ready data" means at Kobie — ensuring our behavioral and transactional data is structured, governed, and accessible in ways that support real-time personalization, predictive modeling, and AI-assisted marketing workflows.
• Drive the long-term roadmap for data capabilities that help clients transform unified customer data into actionable audiences, personalized experiences, and measurable loyalty outcomes.

• Embed practical AI-driven capabilities into existing data products — identifying where AI should assist, accelerate, or automate across the loyalty and marketing analytics lifecycle, in ways that are durable and commercially scalable.
• Partner with Data Engineering and Data Science to ensure AI and ML outputs — propensity models, segment recommendations, next-best-action signals — are productized as client-facing capabilities with clear value propositions, not internal experiments.
• Actively use AI in your own product workflow; bring firsthand fluency to decisions about where AI belongs in a product and where it doesn't.

• Own pricing and packaging strategies for data-driven offerings — usage-based, tiered, and outcome-based models; define the unit of value and validate willingness-to-pay with commercial teams.
• Improve attach rates and revenue contribution of data products by translating platform capabilities into client-facing value narratives that sales and client services can execute against.
• Own the commercialization framework for new AI-enabled capabilities — from proof-of-concept through pricing validation, GTM readiness, and first client deployment.

• Partner with Client Services to identify repeatable patterns across bespoke client work and convert them into standardized, scalable product offerings — with a clear framework for what gets productized versus what stays custom.
• Build structured intake and prioritization for product signals from Client Services, Business Development, and clients — roadmap direction driven by patterns and commercial opportunity, not reactive one-off requests.

WHAT YOU NEED TO BE SUCCESSFUL

• 8–12+ years of product management experience owning commercial outcomes for data, platform, analytics, or engagement products in technology, martech, or data-driven environments.
• Demonstrated experience defining and scaling products focused on marketing, loyalty, or customer experience outcomes — not just data infrastructure delivery or database-to-database movement.
• Actively uses AI in their own product workflow; can articulate where AI should assist, accelerate, or automate — and may have vibe-coded their own solutions.
• Strong commercial acumen: pricing, packaging, and monetization strategy; hands-on experience with usage-based, tiered, or outcome-based pricing models; has owned the unit-of-value definition for a product.
• Proven ability to influence across complex, cross-functional environments without always having direct authority — drives a sharp point of view from concept through build and first sale.
• Analytical mindset with the ability to translate behavioral data, model outputs, and client feedback into strategic product decisions.

• Experience with behavioral data platforms, customer data infrastructure, or CDPs — how event-level data is collected, governed, and activated for personalization and AI use cases.
• Familiarity with loyalty program data economics — how transactional, behavioral, and engagement signals combine to drive member retention, offer optimization, and lifetime value.
• Experience integrating AI and ML capabilities — propensity models, recommendation engines, next-best-action — into existing product workflows rather than shipping standalone AI features.
• Background in or exposure to agentic AI workflows, real-time decisioning, or AI-assisted campaign and offer optimization.
• Experience in adjacent domains — retail media, fintech data products, adtech, or digital analytics — where data monetization and client-outcome orientation are core to the product model.

This role is not a fit if

• Your product experience is primarily in data engineering or infrastructure delivery — commercial ownership and client outcome orientation are central to this role.
• AI is a talking point on your resume rather than something you actively use and build with today.
• You've managed data platform roadmaps but have never owned pricing, packaging, or attach rate for a product.
• You're most comfortable working within engineering — this role requires sustained fluency with commercial, client services, and executive audiences.