EU Supply Engineering team is expanding its team, with new BIE roles, to build world class solutions supporting its customer fulfillment operations by making optimal inbound & placement decisions.. You will get an opportunity to work on new cross-functional initiatives that are exploratory in nature. You will also get a chance to expand your domain knowledge within Supply Chain optimization, along with opportunity to further up-skill your technical skills, working close to Software Developers & Applied Scientists
Have you ever wondered how Amazon delivers so quickly, or if delivery speeds could improve even further? In Amazon Supply Chain, we drive innovation for our customers to enhance product availability and speed while reducing costs and carbon emissions. This role will give an unique view into Amazon Inventory Placement systems, processes, and operations, enabling collaboration with planning, operations, and technical teams. Our system will be built entirely in-house in partnership with SCOT, will use best technology for automating large-scale business, planning and optimization decisions. EU Placement is proud to be on the forefront of thought leadership in the areas of Ideal Placement and Network Capacity Management and works with some of the best business intelligence engineers, software developers, product managers, research scientists in the industry.
Additional knowledge of programming languages like Java, Python & AWS tools is preferable.
This role will be of your interest if:
• You are a self-starter and like to architect systems involving large scale data processing components and Machine learning/economic and optimization models, workflow orchestration..
• You like to work on innovative & impactful initiatives ($100M+ savings) with faster growth.
• You effectively articulate technical challenges and solutions to non-technical audiences.
• You like to experiment with whatever technology needed to get the job done.
• Like the unique opportunity to work in engineering team, while staying close to the business side of the problem.
• Use Test-Driven Development and Continuous Deployment.
• Expand and optimize our supply chain systems to support a variety of emerging business initiative
Additional knowledge of programming languages like Java, Python & AWS tools is preferable.
Key job responsibilities
• Build models & design systems that enhance Inventory Placement strategy for same day business.
• Drive placement strategy improvements using data modeling and analysis to optimize inventory spread across Amazon facilities, enhancing selection and reducing customer delivery distance.
• Collaborate with technology teams to define key priorities to accommodate growing business needs and implement recommendations through new features and/or configurations.
• Research, develop, document and present new opportunities to all levels of Supply Chain, Finance, Fulfilled By Amazon (FBA) and Retail leadership.
A day in the life
• Collaborate with a diverse team of Business Intelligence Engineers, Data Scientists, Program or Product Managers and Finance Analysts to develop effective metrics and analytical tools that address business goals.
• Partner with Business and Tech teams to prioritize impactful insights.
• Explore systems while performing anecdotes to understand concrete root causes.
• Conduct data driven experiments to accelerate innovation.
• Perform analysis at scale with new metric cuts and/or statistical models.- Bachelor's degree in BI, finance, engineering, statistics, computer science, mathematics, finance or equivalent quantitative field
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with data modeling, warehousing and building ETL pipelines
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
- Experience with SQL
- Experience in the data/BI space- Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
- Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets
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