About the job
At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our mission is to prevent denied entities from transacting with Amazon businesses. We build automatic mechanisms to detect and prevent prohibited transactions with denied entities using a diverse set of algorithms and machine learning techniques. We screen over a billion events every day and develop algorithms which are able to scale and detect suspicious entities . We are still Day 1 and have an exciting road map to build Machine Learning (ML) and Generative AI (LLM) powered detection and resolution systems to help scale Amazon for years to come.
We are seeking an Sr. Applied Scientist to join our team and help tackle challenging problems at the forefront of machine learning and artificial intelligence. Working closely with a multidisciplinary team of engineers, data scientists, and domain experts, you will play a crucial role in defining innovative ML/AI-powered customer experiences and solutions. If you have an entrepreneurial mindset, the technical depth to deliver impactful results, and a passion for innovation, we want to hear from you.
Responsibilities
• Drive the research, design, and development of novel ML/AI models and systems to power critical products and services
• Collaborate cross-functionally to deeply understand business requirements, customer needs, and technical constraints
• Rapidly prototype, test, and iterate on ML/AI solutions, iterating quickly based on data and feedback
• Communicate complex technical concepts to technical and non-technical stakeholders
• Mentor and grow a team of talented ML scientists and engineers
• Stay up-to-date on the latest advancements in AI/ML and identify opportunities to apply emerging techniques
Qualifications
Minimum
• 5+ 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
Preferred
• 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.