Director, Artificial Intelligence

Aptive Environmental•Provo, UT

1d•$200,000•Onsite

About The Position

We are seeking a hard-working, innovative, detail-oriented and creative team player to join our Aptive team! This is a full-time Director, Artificial Intelligence position located in Provo, Utah. Aptive has grown from a 2015 startup into one of the fastest-growing pest control companies in the US, serving 76 markets with 2,600+ employees. Our Pest Intelligence Center already blends AI, technician field observations, customer service data, and environmental modeling to forecast pest activity — and we're looking for the leader who takes that from a promising capability to a durable competitive advantage. This is a new role and a builder's role. You'll define Aptive's AI strategy and then personally help ship it: models that decide how we route technicians, when we treat a property, which customers are at risk, and how our service teams spend their time. Success is measured in operational outcomes — retention, route density, first-time resolution — not in papers or proofs of concept. What the first year looks like Days 1–90: Audit existing AI and data assets, including the Pest Intelligence Center. Deliver a prioritized roadmap with named business owners and defined success metrics Months 3–6: Ship or measurably improve two production models tied to P&L outcomes. Stand up model monitoring and a deployment path Months 6–12: Hire the core team. Establish the intake process by which the business requests AI work, and demonstrate compounding returns on at least one flagship initiative

Requirements

8+ years in data science, machine learning, or AI, including 3+ years leading teams
A track record of ML systems in production that changed a business metric — you can explain the metric, the lift, and how you measured it
Depth in forecasting, optimization, or recommendation systems
Strong Python and SQL; fluency with a modern cloud data stack (we run Snowflake)
Experience with LLM-based applications, and the judgment to know when they're the wrong tool
Ability to translate between executives and engineers, and to say no to low-value AI requests
Hands-on enough to review a colleague's model and debug a pipeline

Nice To Haves

Field service, logistics, home services, route-based operations, or seasonal-demand businesses
Geospatial or environmental modeling
Built a data science function from scratch at a mid-size company
MS or PhD in a quantitative field

Responsibilities

AI strategy and roadmap for the Technology organization, prioritized by business impact and sequenced against real data and platform readiness
The Pest Intelligence Center — evolving pest activity forecasting into proactive service planning across all markets
Applied ML in production: demand and pest-pressure forecasting, route and scheduling optimization, churn and retention modeling, dynamic pricing inputs, and lead qualification for our sales channels
Customer and technician-facing AI: contact center automation, agent assist, and decision support in the technician mobile experience
Team building — hiring and growing a team of ML and data scientists, and setting the standards, tooling, and review practices they work to
Partnership with Data Engineering on the warehouse and feature infrastructure these models depend on
AI governance: model monitoring, drift detection, bias review, data privacy, and a clear position on responsible use of customer data