Applied Scientist, Worldwide Grocery Stores - Data and Science

Amazon"s Worldwide Grocery Stores (WWGS), Data & Science team is seeking an Applied Scientist to join our Sales & Operations Planning (S&OP) and Supply Chain Science team. In this role, you will help build forecasting models that drive labor planning across the Amazon Grocery Network, where forecast misses can lead directly to staffing inefficiencies, higher costs, and degraded customer experience.

You will contribute to the development and deployment of demand and labor forecasting models using Time-series, Bayesian and Structural methods, and Machine Learning. Senior scientists on the team will partner with you to scope problems and review designs, giving you room to build depth in forecasting science and production ML. You will also work directly with engineering partners, product owners, and business stakeholders, so you will see how your models change the decisions they make.

Forecasts directly inform downstream labor and capacity decisions, so understanding how errors affect stakeholders is as important as improving accuracy. You will participate in design and roadmap discussions, communicate clearly with technical and non-technical partners, and develop judgment about the trade-offs in the systems you contribute to.

We are investing in Generative AI to advance forecasting workflows, moving from human-in-the-loop to AI-in-the-loop decision support. Opportunities include automating forecast overrides for known events, identifying persistent bias, and augmenting planner and scientist judgment with agentic tools.

Key job responsibilities

• Develop, evaluate, and deploy components of our demand and labor forecasting models, including statistical time-series, Bayesian, and machine-learning models with distributional objectives, with input and guidance from senior scientists.
• Translate business problems into well-defined scientific solutions with clear objectives, constraints, and success metrics, partnering with senior scientists on the more ambiguous ones.
• Analyze forecast performance and downstream impact on labor planning and capacity decisions; develop metrics that reflect business outcomes, not only forecast accuracy.
• Prototype and evaluate Generative AI approaches in our forecasting workflows and help productionize the ones that succeed.
• Partner with engineering teams to produce models, contribute to data pipelines, and build scalable, maintainable forecasting systems.
• Monitor deployed models, investigate performance issues, and continuously improve model quality and calibration.
• Communicate technical concepts and recommendations clearly through documentation, presentations, and design reviews with scientists, engineers, product managers, and business leaders.
• Contribute to the internal scientific community through knowledge sharing and, where appropriate, research publications.