Amazon Sr Applied Scientist Amazon 

United States, Washington, Seattle 

769503345

Key job responsibilitiesKeeping a department-wide view, you will focus on the highest priorities and constantly look for scale and automation, while making technical trade-offs between short term and long-term needs. With your drive to deliver results, you will quickly analyze data and understand the current business challenges to assess the feasibility of different science projects as well as help shape the analytics roadmap of the Science and Analytics team for Search CX. Your desire to learn and be curious will help us look around corners for improvement opportunities and more efficient metrics development.
In this role, you will partner with data engineers, business intelligence engineers, product managers, software engineers, economists, and other scientists.A day in the life
You are have expertise in Machine learning and statistical models. You are comfortable with a higher degree of ambiguity, knows when and how to be scrappy, build quick prototypes and proofs of concepts, innate ability to see around corners and know what is coming, define a long-term science vision, and relish the idea of solving problems that haven’t been solved at scale. As part of our journey to learn about our data, some opportunities may be a dead end and you will balancing unknowns with delivering results for our customers. Along the way, you’ll learn a ton, have fun and make a positive impact at scale.

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- 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 machine learning systems such as profiling and debugging and understanding of system performance and scalability
- Experience with popular deep learning frameworks such as MxNet and Tensor Flow.
- Experience with fine tuning Large Language Models and Retrieval-Augmented Generation is advantageous