AmazonPosted 5d ago

Sr. Data Scientist, Amazon Pay Data Products

Sr. Data Scientist, Amazon Pay Data Products at Amazon scores 96 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

IN, KA, Bengalurumidfull-time

AI in this role

pytorchmlflowsagemaker

ml-ops

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.

We are seeking an exceptional Data Scientist III to drive innovation in machine learning and artificial intelligence solutions while leading high-impact initiatives across the organization.

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-service analytics tools. Our expanded capabilities cover data science and Gen AI wherein we have built our first suite of multi-agent systems.

Basic qualifications

- 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 4+ years of data scientist experience
- Experience with statistical models e.g. multinomial logistic regression
- Knowledge of AWS tech stack (e.g., AWS Redshift, S3, EC2, Glue)
- Track record of developing end-to-end ML solutions that drive business impact

Preferred qualifications

- 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience
- Experience managing data pipelines

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.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Ml OpsPyTorchMlflowSagemaker

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
Walk me through how you've used PyTorch in your day-to-day work.
What are the limits of Mlflow that you've run into, and how did you work around them?
What's a project where you used Sagemaker hands-on?
How would you decide a model or AI system is ready to ship?

Adapt your resume

List these exact terms on your resume: Ml Ops, PyTorch, Mlflow, and Sagemaker. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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