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 machine learning models that improve how the Amazon Grocery Network plans and stocks its stores, where gaps between plan and reality lead directly to out-of-stocks, wasted product, higher costs, and degraded customer experience.

You will contribute to the development and deployment of models across a range of grocery supply chain problems, including demand forecasting, customer preference modeling, and improving product availability, using time series, Bayesian and structural methods, and machine learning. You will work alongside senior scientists who will help you scope problems, review your designs and code, and grow your depth in supply chain science and production ML — and you will work closely with engineering partners, product owners, and business stakeholders to deliver measurable impact.

Our models inform planning and inventory decisions across the grocery supply chain, many of them carried out by partner teams and the systems they own, so understanding how model errors land on stores, planners, and customers matters as much as improving offline metrics. 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 supply chain workflows, moving from human-in-the-loop to AI-in-the-loop decision support. Opportunities include automating routine planner interventions, surfacing recurring sources of operational defects, and augmenting planner and scientist judgment with agentic tools.

Key job responsibilities
- Develop, evaluate, and deploy components of machine learning and statistical models for grocery supply chain problems, including demand forecasting, customer preference modeling, and product availability, with input and guidance from senior scientists.
- Build models and mechanisms that reduce out-of-stocks and shrink, including identifying and helping correct upstream data and process issues that degrade them.
- 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 model performance and downstream impact on inventory, availability, and capacity decisions; contribute to metrics that reflect business outcomes, not only offline model accuracy.
- Prototype and evaluate Generative AI approaches in our supply chain workflows, including automated interventions, and help productionize the ones that prove out.
- Partner with engineering teams to productionize models, contribute to data pipelines, and build scalable, maintainable science 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. - Master's degree or above in Engineering, Computer Science, Machine Learning, Statistics, Physics, or related fields
- Experience building machine learning models or developing algorithms for business application
- Proficiency in Python, including scientific computing and ML libraries (e.g., pandas, NumPy, scikit-learn)
- Experience with SQL and large-scale data processing on a modern data platform (e.g., Redshift, Spark, EMR, or equivalent data warehouse)