Applied Scientist, Buyer Risk Prevention

- Experience programming in Java, C++, Python or related language
- Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse
- Master's degree in Engineering, Computer Science, Machine Learning, Operations Research, Statistics, or related fields
- Experience building machine learning models or developing algorithms for business application
- Experience implementing algorithms using both toolkits and self-developed code
- Have publications at top-tier peer-reviewed conferences or journals

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Do you want to join an innovative team applying machine learning and advanced statistical techniques to protect Amazon customers and enable a trusted eCommerce experience?

Are you excited about working with large-scale datasets and developing models that solve real-world fraud and risk challenges?

If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right fit for you. We are seeking an Applied Scientist to help develop scalable machine learning solutions that safeguard millions of transactions every day.

In this role, you will partner with senior scientists and engineers to translate business problems into data-driven solutions, build and evaluate models, and contribute to next-generation risk prevention systems, including applications of Generative AI and LLM technologies.

Key job responsibilities
Apply machine learning and statistical techniques to build and improve risk management models

Analyze large-scale historical data to identify risk patterns and emerging trends

Develop, validate, and deploy innovative models under the guidance of senior scientists

Experiment with emerging technologies, including GenAI/LLMs, to enhance automation and risk evaluation

Collaborate closely with software engineers to implement models in real-time production systems

Partner with operations and business teams to improve risk policies and operational efficiency

Build scalable, automated pipelines for data analysis, model training, and validation

Monitor model performance and provide clear reporting on key risk and business metrics

Research and prototype new modeling approaches to improve system performance