Senior Applied Scientist, Buyer Risk Prevention

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 5+ 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.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Do you want to lead the development of advanced machine learning systems that protect millions of customers and power a trusted global eCommerce experience?

Are you passionate about modeling terabytes of data, solving highly ambiguous fraud and risk challenges, and driving step-change improvements through scientific innovation?

If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right place for you.

We are seeking a Senior Applied Scientist to define and drive the scientific direction of large-scale risk management systems that safeguard millions of transactions every day. In this role, you will lead the design and deployment of advanced machine learning solutions, influence cross-team technical strategy, and leverage emerging technologies—including Generative AI and LLMs—to build next-generation risk prevention platforms.

Key job responsibilities
Lead the end-to-end scientific strategy for large-scale fraud and risk modeling initiatives

Define problem statements, success metrics, and long-term modeling roadmaps in partnership with business and engineering leaders

Design, develop, and deploy highly scalable machine learning systems in real-time production environments

Drive innovation using advanced ML, deep learning, and GenAI/LLM technologies to automate and transform risk evaluation

Influence system architecture and partner with engineering teams to ensure robust, scalable implementations

Establish best practices for experimentation, model validation, monitoring, and lifecycle management

Mentor and raise the technical bar for junior scientists through reviews, technical guidance, and thought leadership

Communicate complex scientific insights clearly to senior leadership and cross-functional stakeholders

Identify emerging scientific trends and translate them into impactful production solutions