Applied Scientist I, Buyer Risk Prevention (brp)

Amazon Amazon · Big Tech · IN, KA, Bengaluru · Applied Science

Applied Scientist role focused on building and deploying machine learning models for fraud and risk prevention in an e-commerce environment. The role involves end-to-end ownership of ML systems, leveraging large datasets, and applying Generative AI/LLMs to enhance detection and prevention capabilities. Collaboration with engineering and business stakeholders is key, with an emphasis on scalable solutions and performance monitoring.

What you'd actually do

  1. Own end-to-end development of machine learning models for large-scale risk management systems
  2. Analyze large volumes of historical and real-time data to identify fraud patterns and emerging risk trends
  3. Design, develop, validate, and deploy innovative models to production environments
  4. Apply GenAI/LLM technologies to automate risk evaluation and improve operational efficiency
  5. Collaborate closely with software engineering teams to implement scalable, real-time model solutions

Skills

Required

  • Experience programming in Java, C++, Python or related language
  • Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse

Nice to have

  • Experience implementing algorithms using both toolkits and self-developed code
  • Have publications at top-tier peer-reviewed conferences or journals

What the JD emphasized

  • protect Amazon customers
  • trusted eCommerce experience
  • modeling terabytes of data
  • building state-of-the-art algorithms
  • fraud and risk challenges
  • owning end-to-end machine learning problems
  • customer experience and company profitability
  • design, develop, and deploy advanced algorithmic systems
  • safeguard millions of transactions
  • problem formulation to production deployment
  • build scalable ML solutions
  • Generative AI and LLMs
  • fraud detection and next-generation risk prevention systems
  • Have publications at top-tier peer-reviewed conferences or journals

Other signals

  • protect Amazon customers
  • trusted eCommerce experience
  • modeling terabytes of data
  • building state-of-the-art algorithms
  • fraud and risk challenges
  • owning end-to-end machine learning problems
  • customer experience and company profitability
  • design, develop, and deploy advanced algorithmic systems
  • safeguard millions of transactions
  • problem formulation to production deployment
  • build scalable ML solutions
  • Generative AI and LLMs
  • fraud detection and next-generation risk prevention systems