Walmart is looking to solve the problem of cutting-edge fraud detection while ensuring rigorous model lifecycle practices and oversight across their fraud technology stack.
Requirements
- PhD or Master’s in Computer Science, Statistics, Mathematics, or a related field, with a strong foundation in machine learning, data science, and statistical modeling preferred, or relative work experience.
- Expertise in Fraud Detection & Risk Modeling (Preferred)
- Proficiency in building production-ready ML systems, including real-time and batch inference, feature engineering, and model optimization.
- Familiarity with tools like Python, SQL, Spark, and ML frameworks (e.g., TensorFlow, PyTorch, XGBoost).
- Demonstrated ability to explore and apply advanced ML techniques (e.g., semi-supervised learning, continual learning, anomaly detection) to complex problems, ideally with published work or patents.
- Deep understanding of model governance practices including fairness, explainability, drift detection, and documentation.
- Experience implementing responsible AI frameworks in production environments.
Responsibilities
- Lead the design and delivery of high-performance ML models across fraud use cases like identity verification, payment risk, returns abuse, and synthetic accounts.
- Develop hybrid decision-making systems that combine ML predictions with business logic and rules for improved precision and control.
- Ensure models are production-ready, optimized for both batch and real-time inference environments.
- Partner with engineers to define feature pipelines, scoring interfaces, and data contracts.
- Explore and prototype advanced ML techniques: semi-supervised learning, agentic AI, continual learning, anomaly detection.
- Establish governance frameworks for fairness, explainability, documentation, alerting, and drift detection.
- Build and maintain dashboards and SQL-based analytics for monitoring model health and emerging anomalies.
Other
- Qualifications: PhD or Master’s in Computer Science, Statistics, Mathematics, or a related field, with a strong foundation in machine learning, data science, and statistical modeling preferred, or relative work experience.
- Option 1: Bachelor’s degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 6 years' experience in an analytics related field.
- Option 2: Master’s degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 4 years' experience in an analytics related field.
- Option 3: 8 years' experience in an analytics or related field.
- Ability to translate complex technical concepts into actionable insights for non-technical stakeholders and represent the team in enterprise-wide forums.
