DESCRIPTION

The Planning and Execution team (PLEX) is looking for a Data Scientist to provide analytical support to planning and execution activities throughout North America. PLEX is comprised of high-powered dynamic teams, which are shaping network execution through the development and application of innovative labor & flow planning mechanisms. Our goal is to improve and enhance the Amazon Fulfillment network to ultimately drive the best customer experience in a reliable and cost-efficient manner that is truly world-class.

Creating reliable & scalable forecasting techniques requires a sound understanding of the data and creation of quality input for ML models . Your data analytics will influence the performance of all North America Amazon Fulfillment networks. You will be working on large-scale data-sets and leverage your knowledge in advanced data analysis, ML, statistics, and other scientific tools to provide detailed insights that help scientists in the team to create accurate time-series forecasting models and other value-added research to the business. You will contribute in building scalable scientific models & algorithms and provide visualizations to drive data insight. You will own & drive the strategic insights and recommendations for the business that plays a key role in labor planning.

We are looking for a passionate scientist with a commitment to teamwork. Successful candidates will have a deep knowledge of statistical and data analysis techniques to examine complex massive data. You will have the communication skills necessary to impact and influence leadership & partner teams through technical writings, presentations and discussions. You will learn a lot, grow, and have fun in the process!

Innovation Opportunities & Career Growth
Our business grows fast and we want our employees growing with it too. We provide constant opportunities for growth in our team through regular training, talent development, mentoring, and mechanisms conducive to incubating ideas from the bottom up to showcase your innovations.

Inclusive Team Culture
Here at Amazon, we promote an inclusive and engaging environment. We understand the strength that unique experiences bring to the team and value it. In our team, we uphold that all individuals should feel included, respected, and developed.

Flexibility
It's not the hours that you put into work matters, rather it's the quality of work that you put in. We provide flexibility and support to help you find a balance between your work and personal lives.

This position will be based in Austin, TX We are open to hiring candidates to work out of one of the following locations:

- Austin, TX
- Bellevue, WA
- Nashville, TN
- New York, NY



Key job responsibilities
- Design and implement advanced forecasting models to optimize fulfillment network operations
- Develop scalable ML models and algorithms for time-series forecasting
- Analyze large-scale complex data to make decisions in scientific research projects
- Create visualization tools to provide detailed insights into the data and end-to-end systems that drive strategic decisions in the business
- Lead & partner with research, data engineering and product teams to perform mathematical & statistical research for labor & flow planning and optimization
- Simplify the scientific methodology and data analysis by navigating through the technology complexities, explaining them in plain customer and business context to our partners & customers.
- Lead & partner with software and product teams to support productization and integration of mathematical models and enhanced logic into portal-based tools

About the team
PLEX-SIA (Science, Intelligence, & Analytics) serves as the research, automation, and insight arm of the Planning & Execution team of NA Supply Chain org. PLEX-SIA identifies technical gaps, analytical opportunities, and complex operational and planning trends within NACF, in order to action them strategically and sustainably.

PLEX-SIA Science team is the science wing of PLEX-SIA that specifically focuses on the creation, improvement, and automation of labor planning models and processes. This is achieved through AI and ML modeling, scientific analysis of existing processes, and optimization techniques. Science team partners with tech & non-tech partners to improve existing tech solutions and provide data driven recommendations for strategic model implementations.