Head of AI

Head Of AI

Asset Watch serves global manufacturers by powering manufacturing uptime through the delivery of an unparalleled condition monitoring experience, with a passion to care about the assets our customers care for every day. We are a devoted and capable team that includes world-renowned engineers and distinguished business leaders united by a common goal – To build the future of predictive maintenance. As we enter the next phase of rapid growth, we are seeking people to help lead the journey.

Asset Watch has a unique opportunity to scale how LLMs, agents, machine learning, and data science improve customer outcomes, internal productivity, product differentiation, and operational leverage. The Head of AI manages AI work streams across the company, turns scattered AI experiments into governed and measurable operating capability, and leads the Data Science function.

This is Asset Watch's central strategic leadership role, requiring direct, hands-on involvement. The leader must stay close to the field, understand modern AI and data science deeply enough to scope work directly, and help the company adapt as the technology and vendor ecosystem evolves. Reporting to the CEO, the role demands a blend of strategic vision, technical fluency, ethical leadership, and change management skills.

Define and Execute AI Strategy

• Partner with executive leadership to define Asset Watch's AI-native vision, operating model, and continue to build our roadmap heading into 2027 and beyond.
• Identify where AI can create competitive advantage, drive efficiency, and unlock new customer value, which open new revenue streams.
• Keep the strategy current as AI capabilities, tooling, and vendor constraints change.

Lead the AI and Data Science Organization

• Build, lead, and develop the team across Machine Learning Engineering, Machine Learning, and AI Engineering.
• Recruit, develop, and retain high-performing data scientists, ML engineers, and AI engineers.
• Establish clear ROI-based goals, accountability, technical standards, and leadership coverage as the team scales.

Run Intake, Prioritization, and Governance

• Clarify incoming requests by outcome, owner, data dependency, business impact, and build-vs-buy path.
• Establish guardrails for AI tools, agents, model usage, data access, and acceptable use without slowing down adoption.
• Set AI Strategy and OKRs in partnership with senior leadership and translate them into measurable team goals with proven ROI.

Advance ML, MLOps, and Applied AI

• Guide development of physics-based models that improve Asset Watch's reliability intelligence, including anomaly detection, ranking, explainability, and alert quality.
• Ensure production ML systems are monitored, repeatable, and operationally reliable.
• Drive AI engineering work including agentic workflows, internal productivity tools, and customer-facing experiences.

Partner Across the Business

• Work with Product and Engineering to turn AI opportunities into scoped bets with clear owners and delivery paths.
• Partner with GTM, Customer Success, and Operations to identify high-leverage AI opportunities and improve field workflows.
• Collaborate with HR, finance, supply chain, and customer support to implement AI-driven automation.

Measure Impact and Communicate Up

• Define how AI impact is measured and connect AI investments to customer outcomes, efficiency, and revenue.
• Maintain a clear narrative for the CEO, board, and cross-functional leaders on priorities, progress, and tradeoffs.
• Evaluate vendors and tooling; recommend when to build, buy, or combine approaches.

Experience

• 10 or more years leading AI, machine learning, data science, or adjacent data or software technical teams.
• Proven track record setting technological strategy in a fast-moving environment and delivering large-scale initiatives.
• Experience managing cross-functional teams and partnering with senior executive stakeholders.

Technical Depth

• Hands-on fluency with modern AI and data science, enough to scope work, evaluate quality, and challenge assumptions.
• Working knowledge of production ML, MLOps, evaluation, governance, and AI systems lifecycle.
• Familiarity with…