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About This Role
The VP, AI, Data & Analytics is RealTruck’s senior executive accountable for turning data and AI into measurable business value. The role leads Supercharge RealTruck with AI, an initiative to embed AI across how the company designs, builds, sells, and supports products, and heads the BI & Analytics function. The first mandate is to build on an established, high-performing BI team: strengthen reporting, advance analytics maturity, and make trusted data the fuel for AI. The second is to build AI capability from the ground up, including governance, a roadmap, the team, platforms, and a culture of adoption. Working with executive, business, IT, Finance, Legal, HR, and global stakeholders, the VP sets the vision, secures investment, and holds the organization accountable for results.
Key Responsibilities
Lead the Supercharge RealTruck with AI Strategic Initiative
- Serve as the accountable executive owner of Supercharge RealTruck with AI, defining its vision, multi-year roadmap, investment case, and value targets in alignment with RealTruck's enterprise strategy and priorities across e-commerce, supply chain, manufacturing, engineering, sales, and customer experience.
- Lead the initiative's governance and operating cadence, including an executive steering committee, portfolio prioritization, funding decisions, and regular progress, risk, and ROI reporting out to the executive leadership team, CEO and Board as appropriate.
- Build and manage a balanced AI portfolio spanning quick-win productivity gains, process automation and agentic workflows, and transformational use cases that create competitive advantage or new revenue.
- Define enterprise KPIs for the initiative (e.g., productivity hours returned, cost savings, revenue impact, cycle-time reduction, adoption and AI maturity) and ensure benefits are validated with Finance and tracked through realization.
- Act as the company's chief storyteller for AI, communicating progress, wins, and lessons learned to employees, leaders, and external stakeholders to build momentum and confidence.
Set Enterprise AI Strategy and Governance
- Own RealTruck's enterprise AI strategy and operationalize the established AI framework, governance model, architecture standards, and value-validation process across all business units and regions.
- Serve as a trusted advisor to the executive leadership team, translating AI capabilities, limitations, and risks into clear business impact, and making recommendations on prioritization, build-vs-buy, and investment decisions.
- Partner with business leaders to analyze processes, identify and prioritize AI opportunities, and ensure every initiative has clear business requirements, a business case, success measures, and an accountable business owner.
- Chair or co-chair the responsible AI governance body with Cybersecurity, Legal, Privacy, and HR, overseeing data privacy, model risk, acceptable use, ethics, and security guardrails, and ensuring compliance with applicable international regulations, including GDPR and the EU AI Act.
- Engage global and regional business teams to address local requirements, regulatory considerations, and adoption needs while maintaining alignment with enterprise standards.
Drive Enterprise AI Adoption and Change
- Build a culture of AI curiosity, experimentation, and responsible use at every level of the organization, leading by example as RealTruck's most visible AI advocate.
- Sponsor and scale RealTruck's AI Champions program, cultivating an engaged, global community of advocates who share best practices, showcase wins, and accelerate grassroots adoption.
- Direct enterprise-wide AI enablement programs, including platform rollouts, role-based training curricula, communications, playbooks, reusable prompt libraries, and success stories.
- Drive adoption and measurable value from Claude, Microsoft Copilot, and future enterprise AI platforms, holding leaders accountable for adoption within their teams.
- Partner with HR and Learning & Development to build AI and data fluency into leadership development, onboarding, and workforce planning, and to prepare the organization for evolving roles and skills.
- Use adoption analytics and user feedback to identify barriers and continuously raise enterprise AI maturity.
Build and Scale the AI Platform and Delivery Capability
- Stand up RealTruck's AI Enablement function, including its team structure, operating model, intake and delivery processes, and partner ecosystem.
- Own the enterprise AI platform strategy and business administration for Claude, Microsoft Copilot, and future platforms, including licensing, vendor management, user lifecycle, cost optimization, and knowledge repositories.
- Partner with Enterprise Architecture, Infrastructure, Cybersecurity, and external providers to deliver secure, scalable AI capabilities, including AI agents, MCP servers, integrations, APIs, and embedded AI within core business systems.
- Maintain enterprise AI documentation, prompt libraries, GitHub repositories, and reusable assets that accelerate delivery and reduce duplication.
- Continuously evaluate emerging AI technologies and market trends, and recommend opportunities that improve productivity, customer experience, and competitive position.
Lead Enterprise Business Intelligence and Analytics
- Lead and develop RealTruck's established Data Analytics and BI team, preserving its strengths and credibility with the business while elevating its strategic impact.
- Own the enterprise BI and analytics strategy and roadmap, ensuring leaders across every function and region have timely, accurate, and actionable insights to run the business.
- Oversee enterprise reporting, dashboards, KPI frameworks, and self-service analytics, driving a single source of truth for key business metrics in partnership with Finance and functional leaders.
- Advance analytics maturity from descriptive reporting toward predictive and prescriptive analytics in areas such as demand forecasting, pricing, inventory optimization, customer insights, and operational performance.
- Own the enterprise data platform strategy and roadmap partnering with data engineering and technology teams to ensure a modern, scalable data platform and strong data governance, including data quality, master data, lineage, definitions, and access controls.
- Rationalize and optimize the BI tool landscape and analytics spend, ensuring platforms, licensing, and support models are fit for purpose and cost-effective.
Unify Data and AI to Create Enterprise Value
- Position trusted, well-governed data as the foundation for AI, ensuring data readiness is built into AI roadmaps and that AI use cases draw on consistent enterprise data and definitions.
- Integrate AI into the analytics experience, including natural-language querying, AI-assisted insights, and intelligent automation of reporting, to broaden access to data across the organization.
- Create shared standards, talent pathways, and ways of working between the BI & Analytics and AI Enablement teams so the combined organization operates as one integrated Data & AI function.
Organizational Leadership and Talent
- Lead, coach, and grow a high-performing team of leaders and practitioners across BI, analytics, and AI enablement, fostering a culture of accountability, collaboration, and continuous learning.
- Define the organization's structure, roles, career paths, and hiring plan, and attract and retain top data and AI talent.
- Own the combined function's budget, including headcount, platforms, licensing, and partner spend, and ensure strong financial stewardship and measurable ROI.
- Build strategic relationships with technology vendors, consulting partners, and industry peers to extend RealTruck's capabilities.
Global Leadership and Scope
- Hold global accountability for AI, data, and analytics strategy, delivery, and outcomes across all RealTruck business units, brands, functions, manufacturing and distribution sites, and regions, including North America, Europe, and other international operations.
- Establish a global operating model that balances enterprise standards with regional flexibility, defining which capabilities are delivered centrally and which are delivered locally in partnership with regional leaders.
- Lead and develop a globally distributed team, including direct and indirect reports in multiple countries and time zones, fostering a unified culture and consistent ways of working.
- Ensure AI platforms, analytics, and governance comply with applicable local and international laws and regulations, including data privacy, data residency, cross-border data transfer, AI, and employment requirements (e.g., GDPR, the EU AI Act, and works council consultation where applicable).
- Build strong relationships with regional executives and site leaders to understand local business needs, languages, and cultural considerations, and tailor AI adoption, training, and communications accordingly.
- Deliver a consistent global view of performance through harmonized KPIs, data definitions, and reporting across regions, while enabling regional insights.
Drive Revenue Growth Through AI and Data
- Identify, prioritize, and deliver AI and analytics use cases that directly grow revenue and margin, in partnership with Sales, Marketing, E-commerce, Product, and Pricing leaders.
- Apply AI and advanced analytics to improve the customer and dealer experience, including personalized product recommendations, fitment accuracy, cross-sell and upsell, search, and conversion optimization across direct-to-consumer and B2B channels.
- Enable data-driven pricing, promotion, and assortment decisions through price elasticity modeling, promotional effectiveness analysis, and margin optimization.
- Improve demand forecasting and inventory visibility to reduce stockouts, lost sales, and excess inventory across the global supply chain.
- Equip sales, dealer, and customer service teams with AI-powered insights and tools, such as account prioritization, churn and opportunity signals, and AI-assisted customer support, to increase win rates and customer retention.
- Accelerate speed to market by applying AI to product content, marketing, engineering, and new product development processes.
- Explore opportunities to monetize data and AI capabilities through new services, partner insights, or data products where appropriate.
Establish a Data-Driven Business Cadence
- Define and run the enterprise analytics operating rhythm, including daily and weekly operational dashboards, monthly business reviews, and quarterly executive performance reviews, so leaders make decisions from a consistent, trusted set of numbers.
- Partner with Finance to align reporting with the monthly close, forecasting, and planning cycles, and ensure timely, accurate performance reporting to the executive leadership team.
- Establish data freshness, accuracy, and availability standards (service levels) for critical reports and KPIs, and hold the team accountable to them.
- Embed insights directly into leadership meetings and decision forums, moving the organization from reactive reporting to proactive insight, including automated alerts and AI-generated performance summaries.
- Maintain a governed enterprise KPI framework with clear owners, definitions, and targets for revenue, margin, customer, operational, and AI value metrics.
Education and Experience
- Bachelor’s degree required. Master's degree preferred.
- 15+ years of related experience required, including 10+ years of management experience required.
- Demonstrated experience leading enterprise Business Intelligence and Analytics organizations, including reporting, data governance, and advanced analytics.
- 3+ years of hands-on leadership of enterprise AI/ML or generative AI strategy, adoption, or governance, with a track record of moving AI from pilot to scaled, measurable business value.
- Proven ability to lead large-scale, enterprise-wide transformation and change management initiatives across multiple functions and geographies.
- Executive presence with exceptional communication, influence, and storytelling skills, including experience presenting to C-suite and Board-level audiences.
- Strong business acumen and financial management experience, including budget ownership, business case development, and value realization.
Skills, Abilities, and Knowledge
- Ability to develop and communicate a long-term technology vision aligned with business objectives.
- Demonstrated expertise in IT strategy, digital transformation, cybersecurity, technology governance, and organizational leadership.
- Ability to align technology initiatives with business objectives, lead enterprise-wide change, develop high-performing teams, manage complex technology environments, and influence executive decision-making.
- Strong knowledge of IT infrastructure, cloud technologies, enterprise applications, data analytics, cybersecurity, risk management, budgeting, vendor management, and emerging technology trends.
Travel
- Frequent travel may be required.
COMPETENCIES
- Value-based Leadership Behaviors– Demonstrates empathy and compassion towards team members; Develops others and contributes to succession planning; Regularly acknowledges and recognizes; Encourages open communication fostering environment where ideas are freely shared; Creates a collaborative environment; Leads by example demonstrating commitment to teamwork; Sets high standards for accountability; Clearly communicates expectations; Provides support and finds solutions to challenges; Provides frequent feedback; Long term thinker; Fosters creativity and encourages team to explore new ideas; Promotes continuous growth; Celebrates innovative ideas and thinking.
- Attracts Top Talent – Attracts and selects diverse and high caliber talent; finds the right talent to meet the business needs; closes talent gaps with the right balance of internal and external candidates; is a good judge of talent.
- Builds Organizational Capability – Able to develop the skills, knowledge, processes, technology, resources and organizational structure within an organization to enhance its overall performance and competitiveness in line with the overall strategy; Evaluates an organization's strengths and weaknesses, identifies gaps between the current state and the desired future state, and then develops a plan to address those gaps; Identifies the skills needed to support the desired capabilities, and provide training programs and other opportunities for employees to develop those skills.
- Brand & Business Acumen – Knows the purpose of each function and how they play a role linked to the organization’s strategy; Knows key components of how businesses work and how organizations make money; Keeps up with current brand and portfolio product/services knowledge along with possible future trends in the organization and external competition; Uses knowledge of business drivers and how strategies and tactics play out in the market to guide actions.
- Cross Functional Leadership – Ability to lead and collaborate with teams from different departments or disciplines within an organization; Creates synergy among cross-functional team members to achieve a common goal; Understands and communicates how a project or initiative helps each functional area of the organization, proactively keeps everyone well informed regarding progress and potential challenges; Problem solves and resolves conflict quickly; Is organized and effectively leverages resources with their function and outside of their function to deliver results; Able to effectively call upon and leverage resources outside their area of responsibility to generate organizational success. Builds trust quickly and continually motivates and recognizes others.
- Drives Engagement – Structures the work so it aligns with people’s goals and motivators; Empowers others; Makes each person feel his/her contributions are important; Invites input and shares ownership and visibility; Shows a clear connection between people’s motivators and organizational goals.
- Drives Vision & Purpose – Talks about future possibilities in a positive way; Creates milestones and symbols to rally support behind the vision; Articulates the vision in a way that everyone can relate to; Creates organization-wide energy and optimism for the future; Shows personal commitment to the vision.
- Global Perspective – Looks towards the broadest possible view of an issue or challenge; Thinks and talks in global terms; Understands the position of the organization within a global context; Knows the impact of global trends on the organization.
- Strategic Mindset – Sees ahead clearly; can anticipate future consequences and trends accurately; has broad knowledge and perspective; is future oriented; can articulately paint credible pictures and visions of possibilities and likelihoods; can create competitive and breakthrough strategies and plans.
- Thought Leadership – Leverages their deep knowledge on specific areas of the business to generate alternative views, innovative ideas and insightful points to discussions that influence and inspire others; Effectively contributes to strategic discussions; Leads others in the thinking around a given topic along with influencing others to think differently about a specific topic; Has authentic curiosity and is open to listening to others and learning more; Is sought out by others in the company and/or industry for insights and advice.
SUPERVISOR RESPONSIBILITIES
- Vice President: A senior executive leader responsible for setting strategy, driving business results, and overseeing one or more major functions, departments, regions, or business units within an organization.
PHYSICAL REQUIREMENTS
- This position is subject to Sedentary Work: Exerting up to 10 pounds of force occasionally and/or a negligible amount of force frequently or constantly to lift, carry, push, pull or otherwise move objects. Sedentary work involved sitting most of the time. Walking and standing are required only occasionally.
Physical Activities
- This position requires the ability to talk, hear, stand, and use hands, wrists, and fingers to perform repetitive motions for daily computer use.
Visual Acuity
- The worker is required to have close visual acuity to perform an activity such as preparing and analyzing data and figures; transcribing; viewing a computer terminal; expansive reading; visual inspection involving small defects, small parts and/or operation of machines (including inspection); using measurement devices; and/or assembly of fabrication of parts at distances close to the eyes.
Working Conditions
- This position operates in an office environment. This role routinely uses standard office equipment
Perks That Go the Extra Mile :
At RealTruck, we take care of our people—because they drive our success. Enjoy great benefits like medical, dental, and vision coverage, company-paid life insurance and disability, and a 401(k) with company match (eligible the first of the month after you start). Need balance? We’ve got wellbeing days and parental leave. Want to grow? Take advantage of educational reimbursement. Plus, earn referral bonuses and score exclusive employee discounts on RealTruck products. Great benefits. Great people. Even better careers.
RealTruck is the premier manufacturer and digital destination of accessories for truck, Jeep®, Bronco® and off-road enthusiasts around the world. Globally headquartered in Ann Arbor, Michigan, RealTruck’s 6,000+ associates operate from 35+ facilities across four continents. RealTruck’s industry leading product portfolio, which includes the Husky Liners total vehicle protection brand, boasts over 1,000 patents and pending applications. The company’s omni-channel retail approach delivers a seamless consumer experience online at RealTruck.com, as well as through its 12,000+ dealer network and automotive (OEM) partnerships.
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At RealTruck, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
RealTruck AI Hiring
RealTruck has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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