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
Basic Qualifications
- 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
Preferred Qualifications
- 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.
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