Director, Supply Chain Analytics (End-to-End)

JOB TITLE - DIRECTOR OF E2E(END-TO-END) SUPPLY-CHAIN ANALYTICS & DATA SCIENCE

 

Key Responsibilities:

• End-to-End Supply Chain Analytics Strategy: Develop and implement a comprehensive analytics strategy for the entire supply chain lifecycle, leveraging data science, machine learning, and advanced analytics to optimize operations from procurement through to customer delivery.
• Data Integration & Automation: Lead efforts to integrate and streamline data across multiple systems and functions, ensuring that supply chain data is accurate, timely, and accessible for decision-making. Drive the automation of data collection and reporting processes to enhance operational efficiency.
• Advanced Analytics & Machine Learning: Oversee the application of machine learning algorithms, predictive analytics, and optimization models to forecast demand, optimize inventory management, improve production scheduling, and enhance distribution planning. Leverage advanced analytics to identify cost-saving opportunities, mitigate supply chain risks, and improve service levels.
• Supply Chain Optimization: Use data science techniques to solve key supply chain challenges such as demand forecasting, inventory optimization, capacity planning, logistics optimization, and supplier management. Provide actionable insights to drive efficiency and cost savings across the supply chain network.
• End-to-End Visibility: Ensure real-time, end-to-end visibility across the supply chain by developing advanced dashboards and reporting tools that track key performance metrics (KPIs), such as lead times, on-time delivery, cost-to-serve, and inventory turns.
• Collaboration & Cross-Functional Leadership: Partner with key stakeholders in operations, procurement, logistics, IT, and finance to ensure alignment on analytics objectives, data requirements, and KPIs. Influence senior leadership with data-backed recommendations that drive strategic decision-making across the organization.
• Data Governance & Quality Assurance: Establish data governance frameworks to ensure the quality, consistency, and accuracy of supply chain data across various systems. Oversee data management processes and ensure compliance with data privacy regulations.
• Team Leadership & Development: Build, lead, and mentor a high-performing team of data scientists, data analysts, and supply chain experts. Foster a culture of innovation, continuous improvement, and collaboration within the team to stay at the forefront of supply chain analytics.
• Continuous Improvement & Innovation: Stay up to date with the latest trends in data science, analytics, and supply chain technology. Identify opportunities for process improvements, automation, and digital transformation within the supply chain. Drive the adoption of AI, machine learning, and other emerging technologies to enhance supply chain capabilities.
• Project Management & Execution: Oversee the successful execution of analytics-driven projects, ensuring that key initiatives are delivered on time, within scope, and aligned with business objectives. Ensure that projects are scalable and can be implemented across the global supply chain network.

Key Skills & Qualifications:

• Experience: 10+ years of experience in data analytics, data science, or supply chain management, with at least 5 years in a leadership role driving analytics strategies within global supply chains. Strong experience in implementing analytics solutions at scale.
• Supply Chain Expertise: In-depth understanding of supply chain processes, including procurement, manufacturing, logistics, inventory management, demand forecasting, and distribution. Knowledge of key industry trends such as digital supply chain, IoT, and blockchain is a plus.
• Data Science & Advanced Analytics: Proven experience in applying data science and advanced analytics techniques, including machine learning, predictive analytics, optimization models, and simulation. Strong knowledge of data analytics tools (e.g., Python, R, SQL, TensorFlow, Spark) and machine learning frameworks.
• Data Management & BI Tools: Expertise in data integration, data warehousing, and business intelligence (BI) platforms (e.g., Tableau, Power BI, QlikView). Familiarity with ERP systems (e.g., SAP, Oracle) and supply chain management tools is preferred.
• Leadership & Cross-Functional Collaboration: Strong leadership skills with the ability to manage and mentor a team of data scientists and analysts. Proven ability to collaborate with c