Sr. Supply Chain Manager - Amazon Air, Amazon Air

Amazon Air seeks a Sr. Supply Chain Manager to own the analytical work that keeps millions of packages moving on time. The network flies more than 100 flights a day across North America and is growing fast.

This role sits within the Air Customer Experience team and combines data analysis, product thinking, and clear writing. The work helps Amazon Air make faster, more confident decisions during disruptions, planning cycles, and periods of rapid growth. A core part of the job is mapping and managing upstream and downstream planning dependencies across the network, from flight scheduling through linehaul recovery. This surfaces risk before it reaches the customer.

Every decision comes down to one question: will the customer get their order on time? Most of the work is proactive. You will use forecasting and modeling to show where the network is headed and where it is fragile, shaping planning inputs before schedules lock. When the network is under pressure, you will make real-time trade-off calls. You will size the volume at risk, model delivery performance against early action, and quantify the cost of waiting.

This role also requires clear, structured writing. You will write business requirements documents and product proposals that turn operational insights into tool investments and capability improvements. The team is building new analytics, forecasting, and modeling products for the network. You will define what those products should do and make sure they get built.

This role partners with flight planning, linehaul operations, network design, and data science teams across Amazon Air.

Key job responsibilities

1. Build and maintain delivery risk models that identify which routes and time windows carry the highest miss probability ahead of each planning cycle. Translate findings into recommendations the planning team can act on before schedules finalize.

2. Develop trade-off models that quantify the delivery, speed, and volume impact of operational decisions such as flight cancellations, route changes, and linehaul adjustments. Enable leaders to make faster, data-backed decisions during disruptions.

3. Own the analytical inputs to major planning cycles. Identify where the delivery plan is fragile before it runs, not after it misses.

4. Enhance the collection and processing of network and delivery performance data to improve the speed and detail of the insights this role produces.

5. Write business requirements documents, product proposals, and executive-facing briefs that translate operational data into investment decisions and capability improvements. Bridge what the data shows with what gets built.

6. Build relationships with technical and non-technical partner teams to drive correct prioritization of the tools and investments this role recommends.

7. Partner with data science, engineering, flight planning, and linehaul teams to define and improve the analytics tools that support Amazon Air"s delivery performance goals. Apply generative AI tools to accelerate insight generation and automate routine monitoring.

A day in the life

On Monday, you run the delivery risk model ahead of route decisions. It flags three high-risk routes. You dig in, send a one-page recommendation, and two of three adjustments make the final plan.

On Thursday, a hub weighs a flight cancellation. You build the trade-off: packages affected, delivery performance lost, and whether ground transport can recover it. Your summary lets the leader decide in 20 minutes, not two hours.

By week"s end, you draft a proposal for a new analytics tool, using GenAI to speed the analysis, and build relationships that get it prioritized.

No two weeks look the same.

About the team

Amazon Air"s Customer Experience team sets the course for delivering more promises on time across a fast-growing network. We spot risks before they hit customers, and we build the mechanisms that turn a single root cause fix into a lasting improvement across the network.

We work directly with flight planning, linehaul, and data science teams to turn operational insight into action, and we use GenAI to automate manual work so we can spend more time solving the next problem instead of reporting on the last one.

We are a small, hands-on team. Every person owns real outcomes, not just analysis.