Applied Scientist, Worldwide Grocery Stores - Data and Science

1. Job Overview:

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.

2. Key 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.

3. Required Qualifications:

• 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)

4. Required Skills:

• 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)

5. Preferred Qualifications:

• Experience implementing algorithms using both toolkits and self-developed code
• Experience with time-series forecasting or demand planning
• Experience training and deploying models in a cloud environment (e.g., SageMaker, EC2, AWS Batch)
• Publications in peer-reviewed conferences or journals

6. Experience Requirements:

• Not stated in the source description

7. Additional Job Information:

• Employment type: Full-time
• Work mode: On-site
• Notice period: Not stated
• Locations: Seattle, WA, King County, WA; Washington State, United States
• Application instructions and candidate support: Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
• Compensation note provided by employer in source: The base salary range for this position is listed as 136,000.00 - 184,000.00 USD annually for USA, WA, Seattle; final compensation determined based on factors including experience, qualifications, and location. The employer indicates the package will include sign-on payments and restricted stock units (RSUs) and describes benefits in the source.