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Amazon
Amazon's Rest of the World (ROW) Supply Chain Analytics team is seeking an exceptional BIE to join our Delivery Speed Analytics function. In this role, you will be at the forefront of developing solutions that directly impact Amazon's delivery promise capabilities. You will collaborate with Supply Chain and Delivery Experience teams to optimize network speed and enhance customer experience. The role involves data analysis, statistics, machine learning, and usage of AWS services. We are looking for someone who thrives in a fast-paced environment, demonstrates strong project management capabilities, and excels at stakeholder communication. Your ability to understand complex business problems and translate them into analytical solutions will be crucial for success in this role.
We conduct new analysis from ground up, proposing new concepts and technology to meet business needs, and need BIEs who enjoy and excel at diving into data to analyze root causes and implement long-term solutions. As a BIE within the group, you will work closely with program managers, software developers, research scientists, and product managers to analyze massive data sets, identify areas to improve, define metrics to measure and monitor programs, and most importantly work with different stakeholders to drive improvements over time. You will also work closely with internal business teams to extract or mine information from our existing systems to create new analysis, and expose data from our group to wider teams in intuitive ways. As a BIE embedded in the business team you have the opportunity to participate and shape program development as well as leverage your technical skills. This position provides opportunities to influence high visibility/high impact areas in the organization.
This position requires superior analytical thinkers, able to quickly approach large ambiguous problems and apply your technical and engineering expertise to rapidly prototype and deliver solutions. This skill also requires you to work across a variety of teams, including transportation, operations, finance, category, delivery experience, and platform (software) teams. Successful candidates must thrive in fast-paced environments which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, extract and manipulate data across various data marts, and align research focuses on Amazon’s strategic needs.
Key job responsibilities
1.Converting data into digestible business intelligence and actionable information
2.Writing high quality SQL codes to retrieve and analyze data
3.Working with large data sets, automate data extraction, and build monitoring/reporting dashboard
4.Interacting with internal stakeholders to deep-dive outlier events
5.Analyze and solve business problems with focus on understanding root causes and driving forward-looking opportunities
6.Communicating complex analysis and insights to our stakeholders and business leaders, both verbally and in writing.
7.Enable effective decision making by retrieving and aggregating data from multiple sources and compiling it into a digestible and actionable format
8.Should be open to learn and develop skillsets in the latest technologies and analytical techniques
9.You will perform complex data analysis (root cause deep dives, correlations, regressions, simulations, optimization) to identify different opportunities and set priorities to improve them
10. You will be a subject matter expert and understand intricate details of our fulfillment and ordering systems along with their impact on ground-level
11. You will engage with senior leadership on drafting proposals for new concepts, improvement plans, 3-year strategy and outlook.- 5+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with data modeling, warehousing and building ETL pipelines
- Experience in Statistical Analysis packages such as R, SAS and Matlab
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
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