Data Scientist - Marketplace

As a Staff Data Scientist on Marketplace Payments, you will be a senior technical leader driving ML/AI powered payments risk analytics and financial decisioning for Marketplace sellers. This role focuses on building and scaling predictive and machine learning models across payments, credit, and fraud while providing strategic analytical leadership to improve operational excellence and seller financial outcomes.

This role is ideal for a hands-on, seasoned data scientist who applies AI/ML to complex payments and financial risk challenges at scale, while mentoring and developing a team of data scientists and analytics professionals.

What you'll do...

Lead the design, development, and evolution of AI/ML models (predictive, segmentation, anomaly detection) to assess seller risk, credit exposure, fraud, and negative balances

Drive end-to-end analytics and AI decisioning for initiatives such as seller lending, cash-flow optimization, faster payouts, and Marketplace financial services

Apply advanced statistical and machine learning techniques to large, complex datasets spanning payments, fulfillment, orders, and shipments to solve ambiguous payments risk problems

Define and operationalize scalable risk frameworks, translating analytical insights into automated controls and decision systems

Build and maintain dashboards and monitoring frameworks to track risk trends, model performance, alerts, losses, and payments SLAs

Identify, integrate, and enhance high-value internal and third-party data sources to strengthen payments risk signals and feature coverage

Measure and optimize key metrics (alert rates, false positives, losses, operational efficiency) through data-driven experimentation and AI automation

Partner closely with Product, Engineering, T&S, Seller Risk & Performance, Finance, and Treasury to influence the payments risk and data science roadmap

Provide technical mentorship and day-to-day guidance to data scientists; review models and code, and help raise the bar on analytics and ML rigor

Contribute to hiring, onboarding, and developing talent, fostering a culture of ownership, collaboration, and continuous learning

Master’s degree with 7+ year’s experience in a quantitative field (Applied Math, Statistics, Engineering, Machine Learning, Analytics, or related)

6–9 years of experience in data science, applied ML, or advanced analytics roles, including ownership of production-grade models

Strong domain experience in payments, financial services, credit risk, fraud, or risk analytics

Advanced SQL skills and experience working with large-scale, distributed data systems

Proficiency in Python (or similar languages) and modern ML frameworks; experience with Spark or Hive preferred

Solid understanding of eCommerce metrics and key risk indicators, with the ability to define and evolve new ones

Experience driving projects from ambiguous problem statements to measurable business outcomes

Strong communication skills with the ability to influence cross-functional partners and mentor junior team members

Comfortable operating in fast-paced, evolving environments with incomplete information

Walmart US eCommerce is redefining the future of retail by combining scale, data, and technology to help people save money and live better. Marketplace Partner Payments plays a critical role in enabling trusted, fast, and scalable financial experiences for sellers across one of the world’s largest platforms.

Minimum Qualifications...

Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications.

Option 1:
Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 4 years' experience in an analytics related field. Option 2:
Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 2 years' experience in an analytics related field. Option 3: 6 years' experience in an analytics or related field

Preferred Qualifications...

Outlined below are the optional preferred…