Product Operations Leader

Xplor•Remote, OR

23h•$179,000 - $187,000

About The Position

The Product Operations Leader builds and leads the operating system that enables Xplor to use AI responsibly, drive adoption of AI features, and commercialize them into revenue and contribution margin. The role owns shared measurement, value tracking, portfolio governance, intake, cost visibility, and operating cadence across AI-enabled productivity, customer adoption, and commercialization. It creates consistent measurement and improvement across teams using different tools and delivery methodologies, while supporting faster execution without unnecessary friction. This is a hands-on leadership role reporting to the Chief AI Officer. The leader partners with BU presidents, product and R&D leaders, go-to-market, Finance, Security, Legal, Privacy, and the People team. The role builds the function and remains close to data and teams, emphasizing measurable outcomes, decision-ready evidence, and minimal reporting and forums. The leader owns AI value and risk scorecards, productivity and product adoption metrics, commercial measurement, governance and accountability, and the Data and AI vendor and partner review process. They hire and develop a product operations team and apply AI to product operations through reporting, insight synthesis, and workflow automation.

Requirements

At least 8 years of experience in product operations, business operations, strategy and operations, technical program management, product management, or a similar role in a SaaS or technology company.
At least 3 years of experience at a senior or director level.
Track record of building KPI and measurement frameworks connecting product or delivery data to business outcomes such as adoption, retention, and revenue.
Experience working with Finance, Sales, and Marketing on pricing, packaging, launch, or go-to-market performance.
Experience leading cross-functional initiatives across Product, Engineering, Customer Success, and executive leadership, and influencing without direct authority.
Experience establishing governance, decision rights, and accountability mechanisms that teams follow.
Working knowledge of multiple delivery methodologies, including Agile, Scrum, Kanban, and SAFe, and ability to support teams using different methodologies.
Hands-on fluency with product and delivery tooling such as Jira or similar, product analytics, BI, and data integration across systems.
Strong written and verbal communication skills, including presenting data and tradeoffs clearly to executives.
Experience building or leading a team, including hiring and coaching.
Must be legally authorized to work in the country where the role is advertised; visa sponsorship is not offered.

Nice To Haves

Experience with product adoption, monetization, or usage-based pricing.
Experience measuring AI-assisted development or other engineering productivity programs, including frameworks such as DORA and SPACE.
Experience in vertical SaaS or a multi-product portfolio, including products at different stages of modernization.
Comfort using AI tools and automation in daily operating workflows.
Experience with governance, risk classification, or responsible-AI programs, working alongside Security, Legal, and Privacy.
Experience managing a vendor and partner portfolio, including consolidation and contract standards.
Experience with cloud or AI cost management (FinOps), including unit economics and cost per task.

Responsibilities

Build and run a single Finance-partnered scorecard recording AI benefits net of inference, hosting, tools, and support costs.
Produce a quarterly AI board scorecard covering value and risk measures, with a named owner and source system for every measure.
Run a repeatable prioritization and triage process for operations and tooling improvements, assigning owners, expected impact, and result follow-up.
Turn data into concise, decision-ready narratives for the Chief AI Officer and C-suite.
Create a measurement framework linking delivery and AI-SDLC productivity signals to business outcomes.
Help leaders identify where AI productivity gains are occurring, where they are not, and where to invest next.
Coordinate across teams using different delivery methodologies and tools, accommodating their existing ways of working.
Identify bottlenecks that prevent productivity gains from reaching customers and route fixes to the appropriate owners.
Partner with support, revenue operations, finance, and onboarding owners to baseline target workflows on fully loaded cost per resolved outcome while maintaining quality.
Own metrics and standardize reporting on customer adoption and use of AI features across the portfolio at 30, 60, and 90 days.
Build the commercial view of AI and data ARR, paid attach, retention and expansion, win-rate uplift, gross margin per AI feature, and cost per successful task.
Run launch-readiness checks with BU leaders before AI pricing goes live, covering task cost, margin targets, attribution and holdout rules, and sales and customer success enablement.
Maintain decision rights, escalation paths, and clear ownership for data and AI initiatives, including accountable owners, success measures, and review dates.
Maintain a portfolio-level register of Data and AI initiatives and risks, surfacing issues and recommended actions to the Chief AI Officer.
Run monthly Data and AI vendor and partner reviews with Procurement, Finance, and Security, covering spend, overlap, security review, data terms, and consultant knowledge transfer.
Track adherence to agreed standards and policies and report exceptions without creating a bottleneck.
Hire, develop, and lead a product operations team; establish its mandate, operating model, and success measures.
Apply AI to product operations, including automated reporting, insight synthesis, and workflow automation.