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DESCRIPTION

The Amazon Regulatory Intelligence, Safety, and Compliance (RISC) team is seeking an exceptional Applied Scientist to tackle complex compliance challenges. In this high-impact role, you will leverage your deep technical expertise and customer-obsessed mindset to design and evaluate state-of-the-art algorithms in risk modeling, computer vision, and natural language processing.

You will translate complex business requirements into well-defined science problems and work collaboratively with product, engineering, and business stakeholders to drive real-world impact. Staying at the forefront of applied machine learning, you will continuously adapt your skills to tackle new challenges.

Contributing to Amazon's scientific thought leadership, you will author high-quality research papers for internal and external conferences. This is an opportunity to apply your technical skills in a fast-paced, highly collaborative environment, making a meaningful difference in the lives of Amazon's customers.

If you have a passion for solving complex problems and a customer-centric approach, we encourage you to apply for this exciting role.


Key job responsibilities
- Explore the use of reinforcement learning, multi-agent systems, and meta-learning to build autonomous AI agents that can adapt to new challenges and support complex decision-making.
- Design and evaluate state-of-the-art algorithms and approaches in risk modeling, using techniques such as graph neural networks, deep learning, and boosted trees to detect anomalies, fraud, and policy violations.
- Develop advanced multi-modal and large language models to classify product content, understand customer intent, and retrieve relevant information.
- Collaborate closely with product managers, engineers, and business stakeholders to validate hypotheses, drive adoption, and maximize the real-world impact of your work.
- Translate complex product and customer experience requirements into well-defined, measurable science problems and KPIs, contributing to Amazon's scientific thought leadership through high-quality research papers.

A day in the life
- Understanding customer problems, project timelines, and team/project mechanisms
- Proposing science formulations and brainstorming ideas with team to solve business problems
- Writing code, and running experiments with re-usable science libraries
- Reviewing labels and audit results with investigators and operations associates
- Sharing science results with science, product and tech partners and customers
- Contributing to team retrospectives for continuous improvements
- Participating in science research collaborations and attending study groups with scientists across Amazon