Applied Scientist - Buyer Risk Prevention

Amazon India Limited; Bengaluru; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Amazon India Limited is seeking an Applied Scientist I for the Buyer Risk Prevention (BRP) team in Bengaluru. You will translate business problems into data-driven ML solutions, build and evaluate models, and contribute to risk prevention systems that safeguard millions of transactions daily.

You will collaborate with senior scientists and engineers to deploy models at scale, experiment with GenAI/LLMs, and monitor performance while partnering with operations and business teams to refine risk

Qualifications

• Master's degree in Engineering, Computer Science, ML, OR, Statistics, or related field.
• Experience building ML models or developing algorithms for business applications.
• Experience programming in Java, C++, Python or related language.
• Experience with SQL and an RDBMS or Data Warehouse.

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.

Skills

Java/C++/Python

SQL & RDBMS

Education

Master's degree in Engineering, Computer Science, ML, OR, Statistics, or related fields

Job description

Applied Scientist I, Buyer Risk Prevention (BRP) 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

Basic Qualifications

• 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

Preferred Qualifications

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

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Experience Level Senior Level