Amazon's Stores Economics and Science (SEAS) team is seeking an Applied Science leader to build and deliver cutting-edge science and engineering solutions to improve the Stores business, focusing on areas like delivery speed, cost-to-serve, seller fees, and emerging machine learning with LLMs.
Requirements
- Graduate education and hands-on experience in machine learning, optimization, causal inference, Bayesian statistics, deep learning, or other quantitative scientific fields is a must.
- 10+ years of building machine learning models for business application experience
- PhD, or Master's degree and 10+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Knowledge of causal inference and forecasting models are preferred.
- Practical knowledge of how we can leverage Transformers, LLMs, or other deep learning techniques for a variety of applications is a must.
Responsibilities
- lead a team of scientists and engineers with backgrounds in machine learning, NLP, IR, statistics, and economics to identify bottlenecks in our business, conceive new ideas to overcome those challenges, and deploy scientific solutions in partnership with product teams.
- developing the scientific models, benchmarks, and services.
- Practical knowledge of how we can leverage Transformers, LLMs, or other deep learning techniques for a variety of applications is a must.
- building machine learning models for business application experience
- Experience with neural deep learning methods and machine learning
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
Other
- be a quick learner and comfortable with a high degree of ambiguity.
- work safely and cooperatively with other employees, supervisors, and staff
- adhere to standards of excellence despite stressful conditions
- communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service
- follow all federal, state, and local laws and Company policies
