# Aptive Environmental — Director, Artificial Intelligence

## Source and posting metadata

- **Canonical requested posting:** https://www.linkedin.com/jobs/view/4475024806/
- **LinkedIn canonical slug:** https://www.linkedin.com/jobs/view/director-artificial-intelligence-at-aptive-environmental-4475024806
- **Verified employer posting:** https://careers.aptivepestcontrol.com/director-artificial-intelligence/job/P1-7118760-0
- **Provider:** LinkedIn, independently verified against Aptive Environmental's official careers site
- **LinkedIn job ID:** 4475024806
- **Company:** Aptive Environmental
- **Title:** Director, Artificial Intelligence
- **Employment type:** Full-time
- **Seniority:** Mid-Senior level
- **Industry shown by LinkedIn:** Consumer Services
- **Location:** Provo, Utah 84604
- **Work model:** Location-specific; the posting says the position is located in Provo, Utah and provides no remote designation
- **Compensation:** $200,000 salary plus annual merit bonus up to 20%
- **Travel:** Not disclosed
- **Applicants:** LinkedIn displayed “Be among the first 25 applicants”
- **Posting time:** LinkedIn displayed “17 hours ago” at approximately 10:44 AM EDT on October 3, 2026; official-search metadata identified October 2, 2026 as the posting date
- **Elapsed time since posting when captured:** Approximately 17 hours
- **Capture generated:** October 3, 2026 at 10:46 AM EDT

## Normalized job description

### Overview

Aptive Environmental is seeking a full-time Director, Artificial Intelligence located in Provo, Utah. Aptive has grown from a 2015 startup into a pest-control company serving 76 markets with more than 2,600 employees. Its Pest Intelligence Center combines AI, technician field observations, customer-service data, and environmental modeling to forecast pest activity.

This is a builder role. The Director will define Aptive's AI strategy and personally help ship models that inform technician routing, property treatment timing, customer-risk identification, and service-team productivity. Success will be measured through operational outcomes such as retention, route density, and first-time resolution.

### What Aptive offers

- $200,000 salary.
- Annual merit bonus up to 20%.
- Group health, dental, and vision plans.
- Pet insurance, life insurance, and employee-assistance benefits.
- 401(k) employer match up to 4%.
- Paid holidays and paid time off.
- Advancement opportunities and company-culture benefits.

### Responsibilities

- Define the AI strategy and roadmap for the Technology organization, prioritized by business impact and sequenced against data and platform readiness.
- Evolve the Pest Intelligence Center's pest-activity forecasting into proactive service planning across all markets.
- Deliver applied machine learning in production, including demand and pest-pressure forecasting, route and scheduling optimization, churn and retention modeling, dynamic-pricing inputs, and lead qualification.
- Deliver customer- and technician-facing AI, including contact-center automation, agent assist, and mobile decision support.
- Hire and grow a team of machine-learning and data scientists while establishing standards, tooling, and review practices.
- Partner with Data Engineering on the warehouse and feature infrastructure supporting production models.
- Establish AI governance covering model monitoring, drift detection, bias review, data privacy, and responsible use of customer data.

### First-year outcomes

- **Days 1–90:** Audit existing AI and data assets, including the Pest Intelligence Center, and deliver a prioritized roadmap with named business owners and success metrics.
- **Months 3–6:** Ship or measurably improve two production models tied to profit-and-loss outcomes, establish model monitoring, and stand up a deployment path.
- **Months 6–12:** Hire the core team, establish the AI-work intake process, and demonstrate compounding returns on at least one flagship initiative.

### Requirements

- Eight or more years in data science, machine learning, or AI, including at least three years leading teams.
- A track record of production ML systems that changed a business metric, including the ability to explain the metric, measured lift, and measurement approach.
- Depth in forecasting, optimization, or recommendation systems.
- Strong Python and SQL plus fluency with a modern cloud data stack; Aptive uses Snowflake.
- Experience building LLM-based applications and judgment about when not to use them.
- Ability to translate between executives and engineers and decline low-value AI requests.
- Sufficient hands-on depth to review a colleague's model and debug a pipeline.

### Nice-to-have qualifications

- Field service, logistics, home services, route-based operations, or seasonal-demand business experience.
- Geospatial or environmental modeling.
- Experience building a data-science function from scratch at a mid-size company.
- MS or Ph.D. in a quantitative field.

## Fit evaluation

- **Positioning track:** Technical manager
- **Central mandate:** Build Aptive's AI function and roadmap while remaining hands-on enough to ship and govern production forecasting and optimization systems tied to operational and P&L outcomes.
- **Weighted evidence score:** 61/66 weighted points (92.4%)
- **Outcome:** FAIL
- **Hard gap:** The role is explicitly located in Provo, Utah. Keith's work-model constraints allow fully remote U.S. roles and hybrid/on-site roles in Massachusetts, but not a location-specific Utah role or a relocation requirement. No remote designation appears in either the LinkedIn or official Aptive posting.
- **Compensation gate:** Pass. The single disclosed annual salary is exactly $200,000, which meets Keith's minimum, and the role adds a bonus up to 20%.
- **Travel gate:** No conflict established; travel is not disclosed.
- **Applicant-count gate:** Pass. LinkedIn showed fewer than 25 applicants.
- **Employer verification:** Pass. The complete JD is live on Aptive Environmental's official careers site.

## Evidence map

| Requirement | Weight | Evidence score | Source-backed evidence |
|---|---:|---:|---|
| AI strategy and business-impact roadmap | 3 | 3 | At Intelligent DataWorks, Keith owned company AI strategy, product vision, architecture, roadmap, product management, delivery, and P&L; at TriMark he secured executive support for an enterprise data strategy and prioritized 50+ use cases and KPIs. |
| Production ML/AI systems with measurable business outcomes | 3 | 3 | AssistX reached production in about eight months, supported 150+ early adopters and pilot users, achieved 99.9% availability, and accelerated selected workflows up to 50×; SuccessKPI's production WFM platform served four customers. |
| Forecasting, optimization, or recommendation depth | 3 | 3 | Keith designed ML forecasting and operations-research scheduling optimization at SuccessKPI using Python and linear programming; at AWS he implemented an EMR/Spark matrix-factorization recommendation system. |
| 8+ years in AI/ML and 3+ years leading teams | 3 | 3 | The career context documents 25+ years building AI/ML, analytics, and data systems and 20+ years leading engineering teams. |
| Python, SQL, and modern cloud data stack | 2 | 2 | Keith has current hands-on Python and SQL plus extensive AWS, Databricks, data-lake, PostgreSQL, Spark, and data-pipeline experience. Snowflake is not documented. |
| LLM application experience and tool-selection judgment | 2 | 3 | Keith personally architected and built the AssistX generative-AI platform using LLM APIs, agents, RAG, LangChain, LangGraph, evaluations, model flexibility, and cost controls. |
| Executive/engineering translation plus hands-on review and debugging | 2 | 3 | Keith has repeatedly combined executive advising, roadmap ownership, architecture, coding, code reviews, engineering leadership, and pipeline/platform delivery across IDW, AWS, SuccessKPI, TriMark, and NorthBay. |
| Team building and AI governance | 2 | 3 | Keith recruited and led 11 engineers at IDW and teams at SuccessKPI and TriMark; at MassMutual he advised on transparency, bias, drift, monitoring, and model governance. |
| Field-service, logistics, home-services, geospatial, or environmental domain | 1 | 0 | No direct pest-control, home-services, route-field-service, geospatial, or environmental-modeling evidence is documented. |
| Build-from-scratch leadership and quantitative doctorate | 1 | 3 | Keith built multiple AI/data functions and platforms from scratch and holds a Ph.D. in Molecular Biology / Microbiology & Immunology. |

## Direct-match strengths

- Unusually close player-coach match: AI strategy, hands-on architecture and implementation, team building, and measurable operational outcomes.
- Direct forecasting and scheduling-optimization experience from SuccessKPI's workforce-management platform.
- Strong production LLM, AI-governance, monitoring, drift, bias, data-privacy, Python, SQL, and cloud-platform evidence.
- Proven ability to build teams and new technical functions while communicating with executives and engineers.

## Material gaps and caveats

- **Disqualifying location conflict:** Provo, Utah; no remote option is stated.
- Snowflake is not documented in Keith's career context, although adjacent modern-cloud-data-stack experience is extensive.
- No direct pest-control, field-service, home-services, geospatial, or environmental-modeling experience is documented; these are listed as nice-to-have rather than required.
- Travel is not disclosed and would need confirmation at 11% or less if the location conflict were removed.

## Artifact metadata

- **Archived JD capture:** This Markdown file.
- **Resume:** Not generated because the role failed the hard location/work-model gate.
- **Cover letter:** Not generated because the role failed the hard location/work-model gate.
- **LinkedIn connection note:** Not generated because the role outcome is FAIL.
- **Google Drive:** Not used.