Sr. Data Scientist, Amazon Pay Data Products

03 ott - Asti
Amazon

Sr. Data Scientist, Amazon Pay Data Products
Job ID: 10554136 | Amazon Pay (India) Private Limited
Amazon Pay strives to be Earth’s most customer-centric payments service. Our mission is to serve customers and merchant partners with the most trusted, friction-less and rewarding payment solutions for their needs on and off Amazon.
Key job responsibilities
Technical Excellence
- Lead end-to-end machine learning projects using PyTorch, AWS SageMaker, and other leading ML frameworks
- Design and implement complex statistical models and deep learning solutions
- Develop and optimize MLOps pipelines for model training, evaluation, and deployment
- Experience with modern LLM frameworks and Generative AI applications
- Expertise in Python, R, and related data science libraries

MLOps & Development
- Build automated ML pipelines using AWS services (CodePipeline, Lambda, Step Functions)
- Implement CI/CD practices for ML model deployment and monitoring
- Create containerized solutions using Docker for scalable model deployment




- Experience with model optimization and hyperparameter tuning using tools like Optuna
- Integrate ML solutions with monitoring tools like MLflow

Business Impact & Leadership
- Partner with stakeholders to translate business problems into technical solutions
- Design and develop business intelligence applications for real-time insights
- Lead technical initiatives and mentor junior data scientists
- Drive cross-functional collaboration to deliver innovative solutions
- Communicate complex technical concepts to non-technical audiences

About the team
The Amazon Pay Data Products team is a central unit that builds and maintains data products supporting Amazon Pay's growth across multiple markets. We operate at scale, processing 150M+ monthly transactions and managing 12 PB of data infrastructure.
Our team consists of Business Intelligence Engineers, Data Engineers, and Product Managers who develop and maintain standardized reporting, data marts, and self

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