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AssetWatch 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 workstreams across the company, turns scattered AI experiments into governed and measurable operating capability, and leads the Data Science function.

This is AssetWatch'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.

WHAT YOU WILL DO

Define and Execute AI Strategy

• Partner with executive leadership to define AssetWatch'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 AssetWatch'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.

WHAT WE ARE LOOKING FOR

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 state-of-the-art approaches including large language models, agentic architecture, and machine learning.

Business and Leadership Skills

• Strong judgment connecting technical work to customer value, revenue impact, cost control, and risk reduction.
• Excellent communicator, able to translate complex AI concepts for non-technical executives and inspire technical teams.
• Commitment to responsible AI practices including data privacy, bias mitigation, and regulatory compliance.

Education

• Bachelor's degree in computer science, data science, AI, engineering, or a related field required.
• Advanced degree (MSc, PhD, or MBA with technology focus) preferred.

NICE TO HAVE

• Background in industrial technology, predictive maintenance, manufacturing, IoT, or condition monitoring.
• Experience with time-series data, signal processing, anomaly detection, or sensor-driven products.
• Experience with AWS, MLOps tooling, cloud data platforms, and enterprise SaaS integrations.

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