The AI CRO for Revenue Teams

Program Manager

Own the customer’s implementation from first ingest to go-live and beyond — turning revenue context into a live, trusted, fully-adopted Quinn deployment.

ROLE

Program Manager · Customer Implementation & RevOps

LOCATION

Remote (Canada) · Vancouver, BC preferred

TYPE

Full-Time

TEAM

Delivery / Customer Implementation · reports into the founding team

v1.0 · quinn-ai.com · Confidential

01 · Role Summary

Quinn AI is building the Agentic CRO — a digital revenue leader that helps B2B companies improve performance through data, automation, and AI. Every customer is onboarded by the same engine they’ll use every day, on one governed star schema, through a single method: two parallel tracks across six phases.

Those two tracks are DATA and CONTEXT. The DATA track — owned by the Data Engineer — builds the pipeline and is concentrated in the early phases. The CONTEXT track is yours. As Program Manager you own the customer relationship and the program end to end: organization setup, knowledge and RAG, KPI discovery and curation, enablement, the trust gates, and ongoing grooming.

Where the Data Engineer is focused on Phases 1–2, you run the full arc — Phases 1 through 6, from the first data ingest through go-live and into ongoing operation. You are the customer’s guide and Quinn’s voice on the ground: you translate revenue context into a working, trusted deployment, run the audits that gate progress, and drive adoption so the team actually uses Quinn. This is a hands-on, customer-facing delivery role — not a back-office coordinator.

Quinn is a seed-funded startup founded by two former Amazon executives, with solid early traction and paying enterprise customers. You’ll work directly with the founders and see your work reach customers within weeks.

02 · Key ResponsibilitiesOwn the Six-Phase Program — Ingest to Go-Live to Ongoing

You run the CONTEXT track across all six phases of the implementation, gating each one on a signed customer sign-off rather than an assumption:

• Phase 1 · Ingest — provision the organization realm, complete Organization Configuration, and seed Quinn IQ with the customer’s org context.

• Phase 2 · Stage — enable the knowledge graph and projection, begin the RAG / context load, and open Early Access with the first Customer Adoption Workshop (CAW).

• Phase 3 · Transform — run KPI Discovery; organize pages, groups and the 15 RevOps domains; curate and prune the KPI library with the customer; assign KPIs to roles, apply templates, load plan / adjusted-plan targets, and run the KPI audit.





• Phase 4 · Enrich — lead model relevance and interpretation, and relay business context into the 15-model enrichment stack (forecast, anomaly, causation, segmentation, propensity, narratives).

• Phase 5 · Derive & Go-Live — compose the first Business Review and Insight Cards; ground Chat (RAG, Question Bank, Deep Research), Skills, MCP and the CRO Agent; set up users, roles and access; and run the go-live CAW, UAT and launch.

• Phase 6 · Ongoing — keep users, roles and subscriptions current, groom the KPI library and keep RAG / context fresh, operate automation, and run recurring CAWs (~every four weeks).

Customer-Facing Revenue Analytics & Enablement

- Guide customers in defining and tracking the right KPIs for revenue performance, sales efficiency, and pipeline health within Quinn AI.
- Analyze customer revenue data to identify trends, risks, and opportunities across sales pipeline, bookings, churn, and retention.
- Improve forecasting accuracy by helping customers refine their revenue models and interpret Quinn’s predictive insights.
- Support Weekly, Monthly, and Quarterly Business Reviews (WBRs, MBRs, QBRs) with best-practice KPI tracking and performance analysis.
- Provide benchmarking insights, helping customers compare their revenue performance against industry peers.
- Develop and implement best-practice playbooks for revenue inspection, business planning, goal setting, and data-driven decision-making.
- Run Customer Adoption Workshops (CAW) that teach users chat, reports, page settings, and MCP — building fluency, not just access.

Trust Gates & Delivery Governance
- Run the trust gates with the customer and get them signed: the Stage data-confidence audit (the star matches source), the Transform KPI audit (calculations validated against source), and go-live UAT.
- Track every phase, deliverable, and task in Quinn’s agentic Implementation Console and Jira, keeping delivery-health scoring and open items current.
- Gate progress on signed audits and confirmed reviews — the customer reviews and confirms; they don’t build.

Voice of the Customer & Product Influence
- Act as a key voice of the customer, working with Quinn’s product and engineering teams to refine forecasting, planning, and decision-support models.




- Collaborate on feature development, ensuring Quinn’s insights align with customer needs and RevOps best practices.
- Help shape Quinn’s AI-driven recommendations for revenue leaders — churn prediction, pipeline optimization, and scenario modeling.
- Partner with engineers to improve Quinn’s data ingestion, KPI calculations, and revenue intelligence.

03 · Required Skills & Experience
- 5+ years in Revenue Operations, Sales Analytics, or Data Analytics, working directly with B2B revenue data.
- Strong SQL skills and comfort defining KPIs, validating data, and interpreting results against source.
- Experience with CRM (HubSpot, Salesforce) and ERP (NetSuite, SAP, or similar) data structures.
- Proven ability to run customer-facing engagements — explaining complex data and translating insight into action.
- Experience owning implementations or programs end to end: planning, stakeholder management, and driving to a go-live date.
- Comfortable using project management tools (Jira, Asana, or similar) and AI tooling to move quickly.
- Strong communication skills and a bias for action.

04 · Nice to Have
- Data visualization tools (Tableau, Power BI, Looker).
- B2B SaaS environments and recurring-revenue models.
- Python or R for statistical analysis.
- Predictive modeling for revenue forecasting and churn analysis.
- Familiarity with star-schema / dimensional data models and data-quality concepts.
- Exposure to LLMs, RAG, agent tooling, or the Model Context Protocol (MCP) — increasingly central to how Quinn connects to customer systems.

05 · What Success Looks Like
- Customers reach go-live on schedule — with the Stage and KPI audits signed and UAT passed.
- The KPI library is curated, role-assigned, and trusted; Business Reviews and Insight Cards land from day one.
- Users adopt Quinn — chat, reports, and MCP — and stay fluent through recurring adoption workshops.
- Quinn’s forecasts and insights are grounded in the real business context you supplied.
- The account is healthy and compounding — grooming, governance, and QBRs keep realized value growing.

06 · Why Join Quinn AI?
- Own the customer outcome for the world’s first Agentic CRO — from raw data to a live, adopted deployment.
- Work directly with experienced founders and engineers who’ve scaled businesses to billions.
- See your work reach customers in weeks, not quarters, with immediate impact.
- Join at the seed stage with meaningful equity and the opportunity to grow with the company.

Interested? Let’s talk about how you’d take a customer from first ingest to go-live.

📌 Program Manager (Canada)
🏢 Quinn AI
📍 Canada