Applied Scientist I, Buyer Risk Prevention (BRP)

Amazon Inc.; Bengaluru; Remote; INR 1,800,000 - 3,200,000; Full time

Job summary

Amazon is seeking an Applied Scientist to help develop scalable machine learning solutions that safeguard millions of transactions daily. You will partner with senior scientists and engineers to translate business problems into data-driven models and deploy them in production.

You will work with large-scale datasets, explore GenAI/LLMs, monitor performance, and collaborate with operators to improve risk policies and operational efficiency.

Qualifications

• Experience programming in Java, C++, Python
• Experience with SQL and an RDBMS like Oracle
• Experience implementing algorithms with toolkit and self-developed code
• Publications in top-tier peer-reviewed conferences or journals

Responsibilities

• Apply ML and statistical techniques to build risk models
• Analyze large-scale historical data to identify risk patterns
• Develop, validate, and deploy models in production with senior scientists
• Experiment with GenAI/LLMs to enhance automation
• Collaborate with software engineers to deploy models in real-time systems
• Build scalable data analysis and model training pipelines
• Monitor model performance and report key metrics
• Research new modeling approaches to improve system performance

Skills

Java

C++

Python

SQL

Tools

Oracle

Data Warehouse

Job description

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

Preferred Qualifications

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

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.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Veterans, military spouses, and people with disabilities are encouraged to apply.