VP of Product

Xplor•Remote, OR

5h•$280,000 - $290,000

About The Position

Xplor is hiring a VP of Product to lead two strategic priorities: accelerating business performance through AI-driven innovation and creating new revenue opportunities through a data-as-a-service (DaaS) offering built on data and insights across its platforms. The role will identify opportunities to simplify workflows, improve productivity and operations through AI agents and automation, and build a network of AI champions across the business. It will also establish frameworks, governance and enterprise controls for safe, responsible and scalable AI deployment, with measurable value and clear quality, cost and risk standards. The VP of Product will guide automation of repeatable processes, establish enterprise operating practices and controls for agents, and oversee AI evaluation, monitoring and spend management. The position will also develop and monetize data products, including defining customer segments, product problems, pricing and packaging, and partnering with Data Engineering on AI-ready data foundations. The role will take products from validation through launch, including go-to-market, enablement and customer success planning, while ensuring privacy, security and compliance across internal automations and data products.

Requirements

12+ years of product management experience, including 5+ years leading product leaders in B2B SaaS or vertical software.
Track record of bringing data, AI or ML products to market and demonstrating revenue impact beyond pilots.
Prototypes with AI coding and agent tools and uses AI daily in their own work.
Experience shipping AI-first products, including agentic workflows and human-in-the-loop design.
Evaluation-driven product practice, including building evaluation sets, setting quality thresholds and monitoring AI in production.
Hands-on experience pricing and packaging AI features, including usage-based models and inference-cost trade-offs.
Working knowledge of generative AI, LLMs, agents, retrieval and evaluation sufficient to challenge engineers and explain trade-offs to executives.
Strong grounding in data foundations, including quality, governance, privacy and AI-ready data.
Experience with responsible AI and regulatory frameworks across multiple regions.
Ability to lead through influence in a multi-business-unit or federated organization.
Clear communication skills, including the ability to teach, simplify and present to executives and boards.
Must be legally authorized to work in the country where the role is advertised; Xplor does not sponsor visas.

Nice To Haves

Experience in a product, strategy, consulting or venture role sizing AI opportunities.
Experience establishing an AI center of excellence or central pod model.
Familiarity with AI management standards such as ISO/IEC 42001 or the NIST AI Risk Management Framework.

Responsibilities

Recruit and train AI champions in every function and business unit, and provide them with playbooks, low-code tools, office hours and a path from idea to approved production workflow.
Recognize and share successful AI adoption, and lead related change management, training, communications and support.
Identify high-volume, repeatable processes with champions across business functions and maintain a company-wide ranked automation backlog based on hours saved, cost, cycle time and quality.
Design AI-native workflows and reusable automation components, establish baselines, and report savings and productivity gains.
Define the operating model for agents and workflows, covering intake, approval, build, test, deployment, monitoring and retirement.
Create a central agent registry with ownership, purpose, data and system access, and risk tier information.
Partner with Security and IT on agent identity, least-privilege access, secrets management, audit logs and kill switches.
Set standards for evaluating and monitoring AI accuracy, drift, cost per task, failure rates and escalation to people.
Prevent agent duplication and sprawl through shared components and retirement of agents that no longer provide value.
Help functions manage AI spend through budgets, model routing and team-level usage reporting.
Develop and monetize data-as-a-service products from systems-of-record data, such as benchmarks, trends, forecasts, risk signals and recommendations.
Define target customers and the problems they will pay to solve.
Own pricing and packaging, including subscriptions, tiers, usage-based APIs, embedded dashboards and premium add-ons.
Partner with Data Engineering to build AI-ready data foundations covering ingestion, transformation, normalization, categorization, entity resolution, quality and lineage.
Use AI to make data understandable through natural-language querying, summaries and explanations.
Take data products from validation to launch, with go-to-market, enablement and customer success plans.
Partner with Legal, Privacy, Security and Risk to establish legal basis, consent and contractual rights for data products.
Set aggregation, anonymization and minimum cohort-size standards to protect customer, cardholder and individual identities.
Keep payment card data out of scope for insight products and meet applicable PCI DSS and regional data-protection requirements.
Apply appropriate governance to internal automations that use customer or employee data.