Manager, Global Incentive Compensation
Salesforce
- Location
- Canada - Toronto
- Work model
- On-Site
- Level
- Mid
- H-1B history
- 498 approvals (FY2023)
- Posted
- Aug 18, 2026
Skills
About this role
To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. Job Category Finance Job Details About Salesforce Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. We're looking for a Product Manager to help shape the future of our sales compensation and revenue planning platforms, blending deep domain expertise in incentive compensation with a strong grasp of applied AI. You'll own product decisions that directly impact how organizations plan, calculate, and optimize sales compensation — while pushing our roadmap toward smarter, AI-powered capabilities. You will utilize your deep technical competencies and functional business expertise to identify, evaluate, and develop systems and procedures that meet business user requirements and follow general IT and Finance audit guidelines. To do this successfully, you will work closely with multi-functional teams to understand business needs, and IT development to provide automated solutions. An ability to understand business process issues and communicate technical solutions to the internal partners is essential to your success in this role. What You’ll Do: Define and drive the product vision and roadmap for sales compensation (incentive comp, quota, territory, or commission management) features Identify opportunities to embed AI/ML into compensation workflows — e.g., anomaly detection in payouts, predictive quota modeling, natural-language plan configuration, or automated compliance checks Partner with engineering, design, and data science to scope, prioritize, and ship features from concept to GA Evaluate and responsibly apply AI capabilities (LLMs, predictive models, agentic workflows) — balancing innovation with accuracy, auditability, and trust, especially given compensation's financial sensitivity Translate complex compensation business rules (accelerators, tiers, contests, clawbacks, multi-currency plans) into clear product requirements Create effective presentation and communicate roadmap, trade-offs, and outcomes to stakeholders and leadership Implement as product owner in the Agile process from requirements gathering to deployment: Deliver quality and clear user stories Effective refinement of user stories with development and QA teams Effectively prioritize work for the scrum team Develop E2E testing strategy with stakeholders and collaborate with Operations (responsible for execution of testing) Work closely with sales operations, finance, and customer-facing teams to understand pain points and validate solutions Define success metrics, run experiments, and iterate based on customer and usage data What You’ll Bring: 4+ years of product management experience, with meaningful time in sales compensation/incentive compensation, or related sales performance management (SPM) domains Prior experience working as product owner on scrum teams Strong analytical skills; comfort working through compensation math, plan logic, and edge cases Excellent cross-functional collaboration and communication skills — you can talk comp plans with finance and model architecture with engineers Excellent oral and written communication skills, including presentation skills and process documentation Hands-on familiarity with AI/ML product development — you understand what LLMs and predictive models can (and can't) reliably do, and