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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.
Key responsibilities
Requirements:
Specialized Training & Skills:
Preferred Skills and Experience:
This position may be available in the following location(s): U.S. - Remote
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.
For U.S. locations that require disclosure of compensation, the starting pay for this role is between $145,000.00 and $165,000.00. The estimated salary range reflects an anticipated range for this position. The actual base salary offered may depend on a variety of factors.
U.S. based employees may be eligible for short-term and/or long-term incentives. They may also be eligible to participate in medical, dental, vision insurance, disability and life insurance, a 401(k) plan and company match, a tuition reimbursement program (select degrees), company holidays, and well-being benefits, among others. U.S. based employees are also eligible to receive sick time, floating holidays and paid vacation.
Job Applicants should be aware of job offer scams perpetrated through the use of the Internet and social media platforms.
To learn more please read Bausch + Lomb's Job Offer Fraud Statement.
Our Benefit Programs: Employee Benefits: Bausch + Lomb
Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.
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