Job Description
About the role
The Leader of Product Operations builds the operating system that helps Xplor use AI responsibly, adopt it widely, and turn it into revenue and contribution margin, with clear governance and accountability for the results. The role covers three connected areas: AI-enabled productivity, adoption of AI features by our customers, and the commercialization of those features. You will own the shared layer that makes all three work including measurement, value tracking, portfolio governance, intake, cost visibility, and the operating cadence that keeps leaders aligned.
Our products and teams span a wide mix of tools and workflow methodologies. You will create a shared layer of measurement, visibility and improvement that works across them, and that product, engineering, go-to-market and executive leaders all trust, and speeds teams up without creating friction.
You will report to the Chief AI Officer and partner closely with BU presidents, product and R&D leaders, GTM, Finance, Security, Legal, Privacy and the People team. This is a hands-on leadership role: you will build the function and stay close to the data and the teams.
How we work: BUs are central's customers. We measure outcomes, not activity. We decide on a demo, an eval or a metric, not a deck. We keep forums and reporting to the minimum that supports a real decision.
What you'll do
Own the measurement and board scorecard
• Build and run a single scorecard with Finance that records every AI benefit, in one engine, net of AI cost (inference, hosting, tools and support).
• Produce the 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, with clear owners, expected impact and follow-up on results.
• Turn data into short, decision-ready narratives for the Chief AI Officer and C-suite.
Measure AI-enabled productivity
• Build a measurement framework that links delivery and AI-SDLC productivity signals (cycle time, throughput, quality, adoption of AI tooling) to business outcomes such as idea-to-customer lead time, PR-to-production rate, change failure rate, escaped defects, rework and capacity moved to customer-facing work.
• Help leaders see where AI productivity gains are real, where they are not, and where to invest next.
• Act as a neutral orchestrator across teams that use different tools and methodologies (Scrum, Kanban, SAFe-style and hybrids; Jira, Azure DevOps and others), meeting each team where it is.
• Find where gains stall before reaching customers (review wait, regression coverage, late QA, launch readiness) and route the fix.
• Partner with function owners in support, revenue operations, finance and onboarding to baseline target workflows on fully loaded cost per resolved outcome, with quality held constant.
Measure AI product adoption and commercialization
• Own the metrics and standardize reporting for how customers adopt and use AI features across the portfolio at 30-60-90 days, from activation and usage depth to retention and satisfaction.
• Build the commercial view with Finance and BU leaders: AI and data ARR, paid attach, retention and expansion, win-rate uplift, gross margin per AI feature and cost per successful task.
• Run the launch-readiness checklist with BU leaders before any AI price goes live: cost per task known, margin target met after inference, attribution rule and holdout set, sales and customer success enablement ready.
Govern Data and AI accountability
• Operationalize and maintain decision rights, escalation paths and clear ownership for data and AI initiatives, so every investment has a named accountable owner, success measures, and a review date.
• Maintain a portfolio-level register of Data and AI initiatives and risks, and surface issues early to the Chief AI Officer with recommended actions.
• Run the Data and AI vendor and partner inventory and monthly review with Procurement, Finance and Security: spend, overlap, security review, data terms and knowledge transfer from consultants.
• Track adherence to agreed standards and policies, and report exceptions without becoming a bottleneck for teams.
Lead the function
• Hire, develop and lead a product operations team, and set its mandate, operating model and success measures.
• Apply AI to product operations itself, including automated reporting, insight synthesis and workflow automation.
Qualifications
What you'll bring
Required
• 8+ years in product operations, business operations, strategy and operations, technical program management, product management, or a similar role in a SaaS or technology company, with at least 3 years at a senior or director level.
• A track record of building KPI and measurement frameworks that connect 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, including establishing governance, decision rights and accountability mechanisms that teams actually follow.
• Working knowledge of multiple delivery methodologies (Agile, Scrum, Kanban, SAFe) and the ability to support teams that use different ones.
• Hands-on fluency with product and delivery tooling (Jira or similar), product analytics, BI and data integration across systems.
• Strong written and verbal communication, including presenting data and tradeoffs clearly to executives.
• Experience building or leading a team, including hiring and coaching.
Preferred
• Experience with product adoption, monetization, or usage-based pricing.
• Experience measuring the impact of 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 your own daily operating workflow.
• 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): unit economics, cost per task.
Success Measurement
1. First 30 Days: Inventory of AI tools, vendors, spend, pilots and production systems complete. Baseline scorecard and product readiness cards published. Steering cadence and decision log running.
2. 60 Days: Board scorecard reported on its first full quarter in partnership with FinOps and BU leaders. Cost visibility by BU and product in place. Adoption and cost per task tracked for every live AI pilot. Stage gates in use for new initiatives.
3. 90 Days: AI ARR, margin and retention-vs-holdout reported for launched features. Efficiency savings documented. Governance service levels published and met. Product operations running with its own efficiency and productivity measures.
Additional Information
To be considered for employment, you must be legally authorized to work in the country where the role is advertised. This applies to remote roles too. Xplor does not sponsor visas, either at the time of hire or at any later time. We're also not able to offer contractor arrangements or Employer of Record engagements for this position at this stage.
Please apply through our careers portal or external job boards only. We're unable to upload emailed applications, as our process requires you to answer screening questions directly.
We also invite you to check out our Candidate FAQs for more information about our recruitment process xplor.com/recruitment-faqs/.
Interview Recording and Background Checks
As part of our hiring process, successful candidates will be asked to complete background and identity verification checks. Many of our roles are remote, and to protect both candidates and our business from recruitment fraud, we may record video interviews during the process. We keep any recordings and related data only for as long as the hiring process is active and do not retain them afterwards.
EEO and Artificial Intelligence
We believe in transparent hiring. We use an applicant tracking system with artificial intelligence-enabled features to support our recruitment process, but our recruitment team makes all hiring decisions after human review. We do not rely on artificial intelligence to make final hiring decisions. Find our Candidate AI Usage guidelines here.
