Principal Supply Chain Data Engineer & Analytics Lead

Bausch + Lomb (NYSE/TSX: BLCO) is a leading global eye health company dedicated to protecting and enhancing the gift of sight for millions of people around the world from the moment of birth through every phase of life. Our mission is simple, yet powerful: helping you see better, to live better.

Our comprehensive portfolio of over 400 products is fully integrated and built to serve our customers across the full spectrum of their eye health needs throughout their lives. Our iconic brand is built on the deep trust and loyalty of our customers established over our 170-year history. We have a significant global research, development, manufacturing and commercial footprint of approximately 13,000 employees and a presence in approximately 100 countries, extending our reach to billions of potential customers across the globe.

We have long been associated with many of the most significant advances in eye health, and we believe we are well positioned to continue leading the advancement of eye health in the future.

The Principal Supply Chain Data Engineer and Analytics Lead is a critical role responsible for building the data foundation, analytics capabilities, and insight-generation engine required to support Bausch + Lomb's supply chain transformation. This role will partner closely with to extract, connect, structure, analyze, and visualize complex supply chain data across enterprise systems.

This role is designed for a highly analytical, business-oriented data professional who can operate across both technical and commercial dimensions. The individual will help convert large volumes of fragmented operational data into actionable insights, predictive analytics, and decision-support tools that improve service, cost, quality, inventory, productivity, and end-to-end supply chain performance.

The successful candidate will bring strong data engineering and supply chain analytics capabilities, preferably with consulting experience or experience working in transformation-oriented environments. This role will support quantitative and commercial decision-making by developing reliable data pipelines, dashboards, analytical models, predictive tools, and AI-enabled insights that help identify opportunities, quantify value, and drive execution across the supply chain network.

Success in this role will be measured by the ability to improve data availability, accelerate insight generation, strengthen forecasting and operational decision support, improve analytics quality, and enable measurable business outcomes across logistics, planning, warehousing, inventory, and customer fulfillment processes.

• Develop and maintain supply chain data models, pipelines, and data structures that enable consistent reporting, analytics, and decision support across logistics, planning, inventory, warehousing, and fulfillment processes.

• Extract, transform, and integrate data from ERP, MRP, DRP, WMS, WCS, TMS, planning, finance, and other enterprise systems to create reliable analytical data sets.

• Design and deliver dashboards, scorecards, visualization tools, and management reporting that translate complex data into clear business insights and recommended actions.

• Analyze large, complex, and often fragmented data sets to identify patterns, root causes, risks, opportunities, and improvement levers across the end-to-end supply chain.

• Develop predictive analytics, simulation tools, optimization models, and AI-enabled use cases that support service, cost, quality, inventory, and capacity decision-making.

• Support opportunity funnel development by quantifying savings opportunities, operational impacts, customer service implications, and commercial trade-offs.

• Create analytical tools that support decisions related to network performance, warehouse productivity, transportation cost, inventory positioning, demand and supply variability, and service performance.

• Translate technical analysis into executive-ready insights, business cases, and recommendations that support transformation governance and leadership decision-making.

• Collaborate with IT and data architecture teams to improve data accessibility, governance, automation, scalability, and long-term sustainability of analytics solutions.

• Contribute to continuous improvement by identifying opportunities to simplify, standardize, automate, and scale supply chain analytics and reporting processes.

• Bachelors degree in supply chain management, Engineering, Data Science, Computer Science, Information…