Director of Data and AI Products

The CDO is responsible for defining and executing the enterprise data strategy for Railinc, a Data as a Service (DaaS) and Software as a Service (SaaS) company serving the North American freight rail industry. The role owns the complete data landscape of the company—how data is collected, organized, governed, analyzed, operationalized, and commercialized. Railinc products improve safety, efficiency, and customer satisfaction across the freight rail industry.

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 Railinc’s 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 Railinc

Knowledge, Skills, abilities/minimum requirements/competencies:
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, standards, and accountability for data across the enterprise
Builds, leads, and develops a multidisciplinary team of engineers, data scientists, analysts, and QA professionals
Simplifies and rationalizes a complex data landscape so that leaders, engineers, and customers trust and rely on the data
Successfully leverages freight rail network data to drive measurable improvements in: safety and risk reduction, network efficiency and performance, service reliability and customer satisfaction; revenue growth and product portfolio growth for Railinc
Ensures analytical insights are actionable, not academic, and embedded into operational workflows and DaaS and SaaS products
Applies AI/ML, optimization, and advanced analytics only where they produce clear value, while appropriately using deterministic or conventional analytic approaches when they are more effective
Ensures AI solutions are explainable, governed, production‑ready, and appropriate for enterprise freight rail customers
Ensures data systems meet enterprise expectations for resiliency, performance, and auditability
Ensures data initiatives are aligned with enterprise priorities and delivery timelines
Establishes clear expectations, delivery standards, and accountability
Balances hands‑on technical credibility with executive‑level leadership
Implements practical governance that enables speed while protecting customer and company interests
Ensures data quality, lineage, security, and compliance are enterprise‑ready
Anticipates and manages data risk in safety‑critical and customer‑facing use cases

The team size that this pers