VP, Revenue Applied AI & Data

Nintex•Bellevue, WA

2d•Hybrid

About The Position

The VP, Revenue Applied AI & Data leads the strategy, architecture, and hands-on execution of AI-driven solutions across the Revenue organization. This role owns the end-to-end applied AI function for Revenue — spanning use-case identification, solution architecture, data foundations, model development, vendor strategy, and budget — and is accountable for delivering scalable, production-ready AI capabilities that drive measurable business outcomes. Operating with a lean, production-first delivery model, the VP both sets direction and personal builds, ensuring AI moves from concept to deployed, monitored, value-generating systems embedded in everyday Revenue workflows.

Requirements

Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field (or equivalent practical experience).
12+ years building and deploying applied AI, machine learning, or data engineering systems in production environments, with 5+ years leading high-performing technical teams or functions.

Nice To Haves

Advanced degree (MS or PhD) in Computer Science, Machine Learning, Data Science, or a related discipline.
Demonstrated experience designing and shipping LLM-powered and agentic systems to production at scale (prompt orchestration, retrieval, evaluation, and monitoring).
Background in mid-market and/or private equity–backed SaaS environments, with an understanding of their operating cadence, growth expectations, and capital discipline.
Hands-on experience with GTM / Revenue systems (e.g., CRM, CPQ, customer success and billing platforms) and the data they generate.
Experience with modern data platforms and tooling (e.g., cloud data warehouses/lakehouses, ELT/transformation, orchestration) and MLOps/LLMOps practices.
Familiarity with workflow automation / iPaaS platforms such as Nintex Automation CE, K2, or comparable tools.
Track record of partnering with Security, Legal, and Privacy functions to deploy AI responsibly and compliantly.

Responsibilities

Architect, build, and deploy production-grade AI agents and LLM-powered solutions embedded within daily internal Revenue workflows.
Evaluate and execute build vs. buy vs. platform decisions — leveraging Nintex Automation CE and K2 where appropriate, partnering with Product and Engineering on shared infrastructure, and developing custom agentic systems using Claude and other foundation models for unique internal needs.
Own the AI function’s budget, vendor relationships, and technical roadmap for internal applications.
Define, track, and report key performance indicators (KPIs) that link AI investments to measurable outcomes in efficiency, revenue generation, and cost optimization; present progress to leadership on a monthly basis.
Establish clear release standards for AI initiatives, ensuring each deployment has defined outcomes, an accountable workflow owner, and production monitoring in place.
Communicate Revenue AI strategy, investment rationale, and return on investment (ROI) to executive leadership — including the CEO, CFO, and Board — translating technical concepts into audience-appropriate insights.
Design and deploy AI-driven automation across the full customer lifecycle, including pipeline qualification, proposal generation, onboarding, QBR preparation, renewal forecasting, and customer health scoring.
Lead the automation of finance workflows, including reporting, variance analysis, contract analysis, audit preparation, and spend optimization.
Identify and eliminate manual, high-friction operational processes across the organization; enable scalable adoption through self-service AI tools for business teams.
Own and evolve the data engineering foundation, including data pipelines, warehouse architecture, data quality standards, and governed access layers.
Develop and operationalize proprietary models trained on Nintex data (customer, product usage, and operational data) to improve retention, expansion forecasting, and business visibility.
Establish and enforce governance frameworks for responsible, reliable, and scalable AI deployment, including model evaluation, monitoring, and data privacy and security standards.
Operate with a lean, outcome-focused delivery model, driving measurable impact through cross-functional partnerships with business and technical teams.
Define and evolve the organizational structure and reporting model for the AI function in partnership with leadership, aligning resources to areas of highest leverage.
Set and reinforce a high-performance culture emphasizing rapid iteration, production-first delivery, and clear ownership with defined timelines for all initiatives.

Benefits

Global Gratitude and Recharge Days
Flexible, paid time off policy
Employee wellness programs and counseling resources
Meaningful peer recognition and awards
Paid parental leave
Invention/patenting assistance
Community impact, paid volunteer time, and opportunities
Intercultural learning and celebration
Multiple tools through which to learn and grow, and an incredible global community
paid time off
twelve paid holidays
401(k) with employer match