Product Manager, Incentives

Description

Hello, we're Augeo – we are a global leader in enterprise-level engagement and loyalty platform technology, delivering compelling experiences and fostering meaningful connections for employees, consumers, members and channel partners across many industries and verticals. We serve hundreds of clients, including 70+ Fortune 500 companies, representing millions of end users.

We are thought leaders and disruptors who think differently and creatively, built by entrepreneurs, operators and innovators. At Augeo, we're passionate about providing an inclusive workplace that values diversity and our sustainability initiatives. Everyone is welcome - our employees are comfortable bringing their authentic whole selves to work. Be you, reach your full potential.

We work hard, we play hard and, most importantly, we care to our core about our teams and each other. We over-communicate around everything… especially while we navigate our new hybrid world.

Augeo's Applied AI team builds THEO, the orchestration and intelligence layer behind the reward, recognition, incentive and advocacy programs our clients run. We are hiring a Product Manager to own the Incentives domain: the THEO capability set that brings intelligence and automation to performance incentive programs, enabling organizations to configure programs, evaluate participant performance, identify earned outcomes, and orchestrate rewards and payouts at scale.

• →Own the product vision, strategy, and roadmap for the Incentives domain, from problem discovery through shipped, measured outcomes. Your primary success metric is incremental engagement per reward dollar: more of the behavior a client is paying for, at a lower cost per action
• →Alongside our Intelligence and ML team, define how AI and ML models select, personalize, and sequence incentives (rewards, points, offers) to maximize engagement and program ROI for clients and partners
• →Partner with data science and engineering to specify the incentive decisioning engine: the inputs it draws from THEO's user profiles and behavioral signals, model evaluation criteria, and production quality bars
• →Translate incentive economics (budgets, redemption rates, breakage, partner cost structures) into product requirements that balance member value with program sustainability
• →Design and prioritize the incentive catalog and configuration experience so clients and partners can launch and manage programs with minimal manual overhead
• →Build the experimentation practice for incentives, using holdout tests and uplift modeling to measure what a reward actually caused rather than what merely correlated with it. Prove which incentive strategies change behavior, and kill the ones that don't
• →Evaluate where generative AI and agentic workflows can automate incentive content generation, catalog curation, or program configuration, then ship the highest-leverage ones first
• →Turn client, partner, and internal stakeholder needs into precise, prioritized requirements with clear success metrics
• →Ensure incentive products meet privacy, consent, and data governance requirements across enterprise client environments
• →Represent the Incentives pod in cross-team planning, clearly communicating tradeoffs, risks, and progress to engineering, design, and commercial leadership

• 2–4 years of product management experience, including owning features from definition through launch and iteration.
• Product fundamentals: writing clear requirements, prioritizing a backlog against limited engineering capacity, running discovery, and defining success metrics before you build.
• Experience working on data- or ML-driven products, such as ranking, recommendations, personalization, or pricing, and comfort with a fast build-measure-learn cycle with frequent experimentation.
• Working knowledge of AI/ML concepts and a desire to go deeper: you can work with data scientists and engineers on model evaluation, prompting, and system design without being an ML engineer yourself.
• Analytical rigor: comfortable defining metrics, digging into data, and making prioritization calls from evidence rather than opinion.

• Experience contributing to a generative AI or LLM-based feature in production.
• Experience with loyalty, rewards, incentive, or engagement platforms.
• Exposure to experimentation and personalization methods such as A/B or holdout testing, uplift modeling, or recommendation systems.
• Familiarity with incentive and reward program economics, such as points liability, breakage, or redemption modeling.
• Hands-on use of AI-powered development and prototyping tools (e.g., Claude Code) to accelerate product discovery and delivery.