Job Description
DESCRIPTION
The Amazon Payment Acceptance & Experience team is responsible for how Amazon’s customers pay on Amazon’s online and physical stores and through Amazon’s services around the globe. We recognize that payment preferences and regulations vary widely from one region to another, and we obsess over not just how customers want to pay today, but also the ways they will want to pay years in the future. The vision for the PAE Data Science (DS) team is to transform payments data into insight for business decisions, tools for controllership of payments metrics, and models to improve customer payment experiences, such as purchase success rate (PSR) and payment method adoption. We also seek opportunities to improve PAE productivity through automation. PAE Data Science combines the unique skills sets of data science and engineering disciplines to generate value for payment experiences and partner management.
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.
The right candidate is willing to experiment with new tools & technology, transform ambiguous business problems into scientific solutions that are delivered iteratively, and obsesses over incremental improvements in existing solutions to address customer pain points.
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.
If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!
BASIC QUALIFICATIONS
- 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.
Job Tags
Full time, Temporary work, Seasonal work,