Director of Data, Analytics & AI - Cary, NC

The Director of Data, Analytics & AI will act as the Chief Data Officer (CDO) of the organization. The CDO is responsible for defining and executing the enterprise data strategy. The role owns the complete data landscape of the company—how data is collected, organized, governed, analyzed, operationalized, and commercialized. The CDO will leverage the company’s extensive freight rail and network data assets to drive both operational excellence and commercial growth, expanding the company’s product portfolio and increasing revenue through data‑driven solutions.

CDO will use the vast database of freight rail data applying machine learning, artificial intelligence and general information technology tools and techniques. The role requires balanced expertise in AI/ML, advanced analytics, and conventional information technology, with the judgment to apply appropriately based on business value, risk, and maturity.

This position works closely with customer and internal senior IT, product, and business leaders, as well as project managers and software engineers, to ensure data strategy is used efficiently and productively in design, product delivery, and customer outcomes.

The position will be Director level reporting to the Chief Technology Officer and Vice President of Information Technology.

Job Accountability/Responsibilities

Essential Functions:

• Own and execute the enterprise data strategy aligned with company growth, product roadmaps, and customer commitments
• Maintain a comprehensive understanding of all enterprise data assets
• Define how data should be structured, integrated, and governed to support mission‑critical rail operations and DaaS and SaaS products serving other rail interested parties
• Establish clear frameworks for selecting AI/ML, optimization techniques, or traditional analytics based on effectiveness and reliability
• Lead the strategic use of freight rail network data to drive improvements in: safety and risk reduction; network efficiency and performance; customer service reliability and satisfaction
• Ensure network data is trustworthy, explainable, and scalable for enterprise rail customers
• Translate complex operational data into actionable insights and product capabilities
• Own enterprise data architecture, including ingestion, integration, storage, APIs, and data services
• Establish standards for data models, schemas, interfaces, and domain ownership
• Implement practical data governance, quality management, lineage, and stewardship
• Partner with Security and Legal to ensure compliance with customer contracts, privacy requirements, and access controls
• Lead analytics, data science, and AI/ML initiatives supporting forecasting, optimization, decision support, and automation
• Ensure AI/ML solutions are production‑ready, governed, explainable, and monitored
• Apply engineering discipline to analytics and data science, including testing, versioning, and lifecycle management
• Partner with Product Management, Sales, and IT leaders to expand the company’s data‑driven product portfolio
• Identify opportunities to monetize and/or create customer value from the company’s freight rail data through new analytics‑based features, data products, and services
• Partner with software engineers to ensure data requirements are engineered into platforms and products
• Act as a translator between business needs and technical execution
• Set priorities, standards, and performance expectations for a team of at least eight (8) personnel
• Develop talent and mentor technical and analytical leaders
• Ensure effective collaboration across data engineering, data science, analytics, and QA

Key Measures:

• A clear, enterprise‑wide data strategy aligned to real freight rail operating needs
• Data platforms and practices that are scalable, trusted, and audit‑ready
• Measurable improvements in safety, efficiency, and customer satisfaction
• Expansion of data‑driven products and increased commercial revenue
• A high‑performing, accountable data organization embedded across the whole organization
• Achieves and maintains a comprehensive, end‑to‑end understanding of the company’s data assets, including network, operational, customer, and product data
• Establishes clear ownership,…