Amazon Sr Applied Scientist Amazon Auto Store 

United States, New York, New York 

399759322

Key job responsibilities
As Senior Applied Scientist in Amazon Autos, you will:
- Drive big picture innovations with clear roadmaps for intermediate delivery.
- Determine which areas of research to invest in.
- Effectively communicate complicated machine learnings concepts to multiple partners.
- Identify when to leverage existing technology versus innovate a new technology.- Interface with business customers, gathering requirements and delivering science solutions.
- Utilize your Generative AI, time series and predictive modeling skills, and creative problem-solving skills to drive new projects from ideation to implementation.
- Establish scalable, efficient, automated processes for large scale data analyses, model development, validation and implementation.
A day in the lifeYour science expertise will be leveraged to research and deliver novel solutions to existing problems, explore emerging problem spaces, and create new knowledge. You will invent and apply state-of-the-art technologies, such as large language models, machine learning, natural language processing, and computer vision, to build next-generation solutions for Amazon.
You'll publish papers, file patents, and work closely with engineers to bring your ideas to production. Additionally, you will mentor Applied Scientists and Software Development Engineers with an interest in machine learning.

- 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 distributed systems such as Hadoop, Spark etc.
- Experience with model and data quality evaluation techniques, generating labelled datasets, and fine-tuning models. Experience with large language models such as OpenAI, Claude, BERT, and vector database technologies