IT Director - Data, Analytics & AI (AFM, Commercial & Engineering)

Job Purpose

The Director of Data, Analytics & AI leads the strategy, delivery, and adoption of enterprise data, analytics, and artificial intelligence capabilities across Aftermarket, Commercial, Engineering, and Electrification domains. This role is accountable for transforming data into trusted insights, predictive intelligence, and AIenabled outcomes that materially improve decision-making, operational performance, and business value. 

The role owns modern data platforms, analytics products, and AI use cases endtoend—partnering closely with business, engineering, IT, and operations leaders to industrialize analytics and embed AI into daytoday workflows. 

Job Duties and Responsibilities

Data & AI Strategy and Leadership 

• Define and execute the enterprise Data, Analytics & AI strategy, aligned to business priorities and digital transformation goals. 
• Establish and maintain a multiyear roadmap for data platforms, analytics, AI/ML, and GenAI capabilities. 
• Serve as a senior advisor to business and functional leaders on where data and AI can unlock measurable value. 
• Build, lead, and develop highperforming global teams across data engineering, analytics, data science, and AI delivery. 

Data Platforms & Architecture 

• Own the design and evolution of enterprise data platforms, including data lakes/lakehouse, analytics environments, and integration patterns. 
• Lead analytics, enterprise data lakes, and curated data products used across functions. 
• Ensure platforms are scalable, secure, costeffective, and designed for analytics and AI at scale. 
• Partner with Enterprise Architecture and Cybersecurity to enforce standards, governance, and data protection. 

Analytics, AI & Advanced Capabilities 

• Identify, prioritize, and deliver highvalue analytics and AI use cases, including:  
• Operational and financial performance analytics 
• Predictive and prescriptive insights for aftermarket, supply chain, and engineering 
• AIenabled forecasting, anomaly detection, and optimization 
• Generative AI for engineering productivity, analytics automation, and decision support 
• Oversee the full lifecycle from experimentation to productiongrade AI solutions. 
• Drive a productbased analytics model—building reusable data assets and AI capabilities rather than oneoff reports.

Data Governance, Quality & Trust 

• Establish enterprise data governance, stewardship, and master data frameworks. 
• Define data ownership, critical metrics, and quality standards to improve trust and adoption. 
• Ensure compliance with security, privacy, IP protection, and responsible AI principles. 
• Measure and improve data quality, lineage, and transparency across domains. 

Value Realization & Adoption 

• Partner with business leaders to translate problems into analytics and AI solutions. 
• Define success metrics and track business value (productivity, cost, quality, revenue, risk). 
• Drive change management and adoption to ensure insights and AI are embedded into daily operations. 
• Lead analytics operating reviews covering adoption, value, risk, and platform health. 

Enabling Platforms & Delivery (Supporting) 

• Ensure core engineering and digital platforms effectively feed enterprise analytics and AI use cases. 
• Collaborate with engineering platform teams (e.g., PLM, DevOps, EV systems) to enable data access and insight generation. 
• Manage vendor relationships, delivery partners, and financial planning for data and AI initiatives. 

Education and Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data, or related field (Master’s preferred). 
• 10+ years of experience in data, analytics, AI, or digital transformation roles. 
• Proven experience delivering enterprisescale data and analytics programs with measurable business impact. 
• Strong understanding of modern data architectures, analytics platforms, and AI/ML concepts. 
• Experience leading global, crossfunctional teams. 

Preferred 

• Handson familiarity with advanced analytics, AI/ML, MLOps, or GenAI use cases. 
• Experience in manufacturing, industrial, or assetintensive environments. 
• Exposure to OT/IT data integration, operational analytics, or engineering data ecosystems. 
• Strong financial and vendor management experience. 
• Measures of Success 
• Business value delivered through analytics and AI initiatives 
• Adoption and trust in data, insights, and AI tools 
• Speed from idea to production for analytics and AI use cases 
• Data quality, governance maturity, and platform scalability 

Skills and Competencies