The Analytics Engineer builds and maintains the data foundations that power advanced analytics, AI‑driven insights, and reporting across Dana’s global supply chain. This role is responsible for transforming raw operational data into reliable, accessible, and well‑modeled datasets—enabling improved forecast accuracy, optimized material flow, enhanced decision‑making, and automated analytics at scale.
The position is highly technical, blending data engineering, data modeling, and analytics to support end‑to‑end supply chain performance.

AI, Data Science & Predictive Analytics

• Build and maintain AI‑driven forecasting, inventory optimization, and supply‑risk detection models.
• Apply data science techniques (regression, clustering, anomaly detection, statistical modeling) to identify process inefficiencies and cost‑saving opportunities.
• Develop predictive insights on demand variability, supplier performance, logistics constraints, and inventory exposure.
• Support AI adoption across plants by training users, interpreting model recommendations, and validating accuracy of automated signals.

Working Capital & Cost Optimization

• Analyze inventory levels, safety stock models, lead times, and cycle stock to reduce working capital without compromising customer service.
• Identify excess, obsolete, and slow moving inventory drivers and propose data backed reduction strategies.
• Support cost reduction initiatives by analyzing transportation cost trends, supply routes, MOQ impacts, and supplier delivery patterns.
• Create scenario based models to evaluate the cost impact of forecast shifts, production changes, sourcing decisions, or logistics contingencies.
• Monitor working capital KPIs and provide actionable insights to MP&L and Finance.

Supply Chain Operations & Analytics

• Conduct deep dive analysis into material flow, capacity constraints, and MRP exceptions using SAP data.
• Develop real time dashboards and exception reports to improve visibility and streamline decision making.
• Partner with planners, schedulers, MP&L managers, and operations leaders to translate insights into executable actions.
• Support new product launches, engineering changes, and supply readiness activities using data driven assessments.
• Participate in continuous improvement projects focusing on parameter optimization, scheduling accuracy, and inventory efficiency.