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Glimpse is an AI platform automating back-office workflows for CPG brands, including deductions management, revenue recovery, and cash application. Since launching in April 2024, the company has grown to 200+ customers and raised $52M from top-tier investors including a16z, 8VC, and Y Combinator.
As Product Operations Manager, you will own how well the product recovers money for customers and ensure learnings flow back into product improvements. You'll operate at the intersection of customers, operations, and product/engineering teams.
Key responsibilities include:
- Monitor the full deduction-to-recovery pipeline across accounts, tracking recovery rate, dispute win rate, deductions as a percentage of revenue, time to resolution, and revenue at risk.
- Analyze recovery and deduction data to identify patterns in underperformance by retailer, deduction reason, or claim type; build dashboards and reporting to surface findings to customers and internal teams.
- Translate operational gaps into prioritized product requirements; partner with product and engineering to validate improvements against metrics.
- Standardize and document recovery workflows, exception handling, and quality checks; identify manual steps for automation.
- Partner with enterprise customers and internal stakeholders to explain recovery performance, answer operational questions, and set expectations with senior leadership.
You should have 4+ years in product operations, revenue operations, strategy/consulting, analytics, or program management. Strong data skills are essential—you must be comfortable with SQL and/or advanced spreadsheets (BI tools a plus). You excel at finding the "why" behind numbers and translating findings into action. Excellent written and verbal communication is critical, as is the ability to work fluidly across customers, operations, product, and engineering.
Nice-to-have experience includes CPG, retail, supply chain, deductions/chargebacks, accounts receivable, fintech/revenue recovery, familiarity with deduction and dispute workflows, exposure to AI/ML products, or prior experience as an early operational hire building a function from scratch.
