Senior Product Manager

Varicent

1d•CA$125,000 - CA$164,200

About The Position

At Varicent, we’re not just transforming the Sales Performance Management (SPM) market—we’re redefining how organizations achieve revenue success. Our cutting-edge SaaS solutions empower revenue leaders globally to design smarter go-to-market strategies, maximize seller performance, and unlock untapped potential. Varicent stands at the forefront of innovation, celebrated as a market leader in the 2025 Forrester Wave Report for SPM, 2023 Ventana Research Revenue Performance Management (RPM) Value Index, Gartner Peer Insights, 2024 Gartner SPM Market Guide, and G2. Our solutions are trusted by a diverse range of global industry leaders like T-Mobile, ServiceNow, Wawanesa Bank, Shaw Industries, Moody's, Stryker and hundreds more. We are entering an exciting stage of growth as we expand our AI product team, backed by strong executive support and a clear AI vision. Our focus is on building practical, high-value AI capabilities that help customers reduce manual work, uncover insights faster, and improve operational decision-making. Our AI product team works on meaningful problems in a complex, high-impact domain, helping shape the next generation of AI capabilities and autonomous agents for enterprise customers. We also work within a modern product development lifecycle where you will have access to modern AI tools that support product discovery, research, rapid prototyping, analysis, documentation, and execution. If you are excited by the opportunity to help shape AI products in a proven enterprise software company with real customer problems, strong leadership commitment, and the tools to move quickly, Varicent is a great place to build. As a Senior Product Manager, AI Adoption & Customer Value, you will lead the 1-to-n growth, adoption, optimization, and ongoing success of Varicent's AI portfolio. This is a senior individual contributor role responsible for ensuring Varicent's AI capabilities are not only launched, but trusted, adopted, and delivering measurable customer and business value. You will work closely with customers and internal teams to turn early product traction into repeatable adoption across Varicent's enterprise customer base. You will partner closely with Product, Product Operations, Engineering, Design, Product Marketing, Go-to-Market, Customer Success, Sales, Support, and customers to validate adoption patterns, identify friction, prioritize improvements, and scale successful AI capabilities. This role is ideal for a product leader who is passionate about AI, customer outcomes, product adoption, value realization, and helping enterprise customers adopt new capabilities in practical, measurable ways.

Requirements

8–10+ years of professional experience, including 6+ years in Product Management, preferably in B2B SaaS, enterprise software, AI, automation, or data-driven products.
Bachelor's degree in Business, Computer Science, Engineering, or a related field required.
Proven experience as a senior individual contributor owning product strategy, execution, adoption, optimization, launch readiness, or value realization for complex product areas.
Strong familiarity with emerging AI concepts such as GenAI, autonomous agents, MCPs, agent orchestration, AI evaluations, enterprise AI governance, trust, explainability, and responsible AI practices.
Experience managing products post-launch, including adoption, optimization, product health measurement, customer feedback, iterative improvement, and scale.
Experience working directly with enterprise customers to synthesize customer feedback into clear product recommendations and roadmap priorities.
Skilled at using data-driven decision-making, experimentation, customer insight, and prioritization frameworks such as RICE, ROI, and Value vs. Effort.
Proven ability to partner cross-functionally with Engineering, Design, Data Science, Product Operations, Product Marketing, Customer Success, Sales, Support, and Go-to-Market teams.
Deep understanding of Agile methodologies and product development tools such as JIRA, Confluence, Figma, Productboard, analytics platforms, or similar tools.
Excellent written and verbal communication, storytelling, and stakeholder management skills, including the ability to influence senior stakeholders, communicate complex ideas clearly, and align executives, engineers, and customer-facing teams.

Nice To Haves

Experience running early access, beta, design partner, pilot, or strategic customer adoption programs preferred.

Responsibilities

Own the AI Adoption & Customer Value Strategy: Own the end-to-end strategy for adoption, optimization, value realization, and scale across Varicent's existing and recently launched AI capabilities, agents, and product experiences.
Define how AI products move from early traction to repeatable customer adoption, retention, and scalable growth.
Prioritize adoption efforts based on customer value, business impact, usage signals, customer readiness, implementation complexity, and strategic importance.
Lead Strategic Customer Adoption Programs: Work directly with strategic customers to understand whether AI products are solving meaningful workflow pain points and delivering measurable value.
Design and lead structured adoption programs that define customer goals, success criteria, feedback loops, adoption risks, rollout needs, and scale-readiness requirements.
Define Adoption, Product Health & Value Realization Metrics: Define what successful adoption looks like across AI products alongside the AI Strategy Product Manager, including activation, engagement, usage quality, retention, workflow impact, and value realization.
Partner with Product Operations, Data Science, Engineering, Customer Success, and Go-to-Market teams to establish dashboards, reporting, feedback loops, and product health measures that inform roadmap and adoption decisions.
Synthesize Customer Feedback & Adoption Insights: Build strong customer relationships to deeply understand user needs, pain points, readiness, and adoption barriers.
Represent the voice of the customer in roadmap discussions and prioritization.
Use product analytics, customer feedback, usage patterns, support trends, and Go-to-Market insights to identify opportunities to improve product value, usability, trust, and adoption.
Partner with the AI Strategy Product Manager: Ensure new AI capabilities are designed with customer adoption, enablement, feedback loops, and value realization in mind.
Partner with Engineering, Design & Data Science: Work closely with Engineering, Design, and Data Science to shape, scope, and deliver prioritized improvements to existing AI products.
Provide product leadership on adoption-related improvements, including usability, explainability, model behaviour, workflow fit, performance, scalability, security, supportability, and customer trust.
Enable Product Marketing & Go-to-Market Teams: Work closely with Product Marketing and Go-to-Market teams to translate early customer wins, learnings, and adoption patterns into repeatable launch and adoption motions.
Contribute to positioning, messaging, enablement materials, demo guidance, FAQs, objection handling, customer stories, internal readiness, and customer-facing assets.
Partner with Customer Success, Sales & Support: Partner with Customer Success, Sales, and Support to understand customer readiness, rollout needs, enablement gaps, support trends, adoption blockers, and expansion opportunities.
Ensure customer-facing teams are equipped to explain, position, deploy, support, and gather feedback on AI capabilities.
Drive Cross-Functional Alignment: Collaborate across Product, Engineering, Design, Data Science, Product Operations, Product Marketing, Go-to-Market, Customer Success, Sales, Support, and leadership.
Communicate adoption progress, product performance, customer learnings, risks, trade-offs, and recommendations across the business.
Influence priorities and foster collaboration without direct authority.
Stay Ahead of Enterprise AI Trends: Contribute to and improve product management practices related to AI adoption, customer validation, launch readiness, feedback loops, product health, value realization, and post-launch optimization.
Build a strong product perspective on enterprise AI trends including GenAI, autonomous agents, AI governance, explainability, responsible AI, and enterprise change management.