Contract Amazon Data Science Jobs in Raleigh, NC

DataStaff, Inc is seeking an AI and Data Product Director for a direct hire opportunity with one of our direct clients located in Cary, NC
             
Job Description:
The Director of Data, Analytics & AI product will act as the Chief Data Officer (CDO) of the organization.  The CDO is responsible for defining and executing the enterprise data strategy, 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. 
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 our 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 customers 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 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 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 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 

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 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
• 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 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

Education, experience, certification/training:

• Bachelor’s Degree in Computer Science, Information Technology or similar relevant technical concentration required
• Minimum of 10 years’ experience in the information technology arena with at least 5 years of recent experience as a mid to senior manager focused on data architecture and data management
• Familiarity with agile methodologies (or similar Development Methodologies) required
• Possess a natural curiosity to experiment with new technologies and systems
• Strong leadership and people management skills required
• Possess a strong commitment and dedication to accomplish established objectives
• Requires excellent communication and interpersonal skills

This opportunity is available as a W2 position with a competitive benefits package.