At Amazon, we are the most customer-centric company on earth. If you'd like to help us build the place to find and buy anything online, this is your chance to make history. To get there, we need exceptionally talented, bright, and driven people. We are looking for a dynamic, organized self-starter to join as an Applied Scientist for Promise Optimization.
The Promise Optimization team seeks to identify the optimal delivery option for whatever a customer wants. We believe that finding an optimal promise (delivery speed) and living up to it consistently improves our customer experience because we increase customer's confidence and trust in Amazon as the one, best option to get what you want, when you want it.
As an Applied Scientist for Promise Optimization, you will spearhead the development and productionization of the latest machine learning models, addressing critical predictive and forecasting challenges. Your role will be pivotal in scaling, automating, and deploying these models, collaborating with a diverse scientific team that includes software engineers, economists, data engineers, and fellow applied scientists.
This position is ideal for a forward-thinking scientist eager to apply the latest breakthroughs in AI and machine learning to solve complex, high-impact problems. Throughout your projects, you'll need to strike a delicate balance between analytical rigor and pragmatism, always prioritizing the delivery of tangible value in response to pressing business questions. You'll have the opportunity to shape the future of our organization through the innovative use of the latests technologies and methodologies.
Responsibilities include:
- Collaborating with Applied Scientists, Business Intelligence Engineers, Data Engineers, Economists, Research Scientists, and Software Development Engineers to design, test, implement, and support state-of-the-art predictive and forecasting models in production software environments.
- Ensuring production models are robust, scalable, and effectively address both business needs and software engineering requirements.
- Leveraging big data and AWS technologies to scale, automate, and productionize core statistical models, enabling rapid deployment of refreshed models.
- Mentoring other researchers on the effective use of these cutting-edge tools.
