Amazon Data Scientist II Payment Acceptance Experience 

United States, Washington, Seattle 

776272078

We’re looking for a Data Scientist capable of using generative AI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems, specifically building quick prototypes for GenAI Agents to automate repetitive tasks. You will develop models that provide insight into customer’s paying behavior, design and run experiments to improve Payments core metrics such as PSR, Total processing volume, and Cost of Payments. You will interact with stakeholders across Marketing, Product, and Software to unearth problems that can be solved better, faster, and at lower cost using latest literature in Machine Learning and GenAI. You will contribute to team’s Data Science roadmap and mentor other Data Scientists in the team.
Key job responsibilities
• Building GenAI Agent prototypes to improve KOW productivity and transform Data Analytics within PAE
• Evaluating Downstream impact of customer’s payments interactions and influencing Amazon-wide metrics to be Payments aware (CP, OPS, DSE etc.)
• Develop machine learning models that inform PAE’s Marketing efforts through customer targeting, incentive optimization, and personalized messaging.
• Identifying new opportunities to influence business strategy and product vision using data science and machine learning.
• Leading the project plan from a scientific perspective on product launches including identifying potential risks, key milestones, paths to mitigate risks, and success metrics.
• Working through significant business and technical ambiguity to continually improve PAE Data Science team’s roadmap with autonomy.
• Coordinating support across engineers, scientists, and stakeholders to deliver ML pipelines, analytics projects, and build proof of concept applications.
A day in the life
Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan.

- 2+ years of data scientist experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Experience applying theoretical models in an applied environment
- Ability to convey mathematical results to non-science stakeholders.
- Experience building data products incrementally and integrating and managing datasets from multiple sources
- Ability to deal with ambiguity and competing objectives in a fast-paced environment.

- 5+ years of a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science experience
- Experience with AWS services including S3, Redshift, Sagemaker, EMR, Kinesis, Lambda, and EC2
- Experience with building GenAI agents for task automation
- Experience in payments and payment products
- Peer reviewed scientific research papers published internally or externally