The Sort Center network is the critical Middle-Mile solution in the Amazon Transportation Services (ATS) group, linking Fulfillment Centers to the Last Mile. The experience of our customers is dependent on our ability to efficiently execute volume flow through the middle-mile network.
The Data Scientist II will design and implement solutions to address complex business questions using advanced statistical and machine learning (ML) techniques, experimentation, and big data. In this role, you will build scalable ML models, apply advanced analysis technique and statistical concepts to draw insights from massive datasets, and create intuitive science models and data visualizations. You can contribute to each layers of a data solution – you will work closely with business intelligence engineers and product managers to obtain relevant datasets and prototype predictive analytic models, and implement data pipeline to productionize your models, and review key results with business leaders and stakeholders. Your work exhibits a balance between scientific validity and business practicality.
To be successful in this role, you must be able to turn ambiguous business questions into clearly defined problems, develop quantifiable metrics and robust machine learning models from imperfect data sources, and deliver results that meet high standards of data quality, security, and privacy.
Key job responsibilities
- Development of scalable data science solutions catering to volume and cube forecasting for NASC Sales and Operation Planning Team.
- Working closely with Network Planners, Product Managers, Data Scientists, Business Intelligence Engineers, and various planning teams to drive business decisions and alignment with business stakeholders.
- Development of scalable data science solutions to audit and optimize our NASC network.
- Development and execution of analytical tools to model our transportation network.
- Contribute to the strategy for network design, prioritize technical and operational initiatives, evaluate and set stakeholders expectations.
