Senior, Data Scientist

Walmart, Inc.

$90K — $180K *
Finance & Insurance
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's/Master's degree in relevant field (Statistics, Computer Science, etc.)
  • 5-7 years of experience in data analytics or machine learning.
  • Proficiency in Python and SQL for data manipulation.
  • Familiarity with distributed computing tools like PySpark or BigQuery.
  • Hands-on experience with Graph Neural Networks and their applications.
  • Experience with Generative AI techniques for decision intelligence.
  • Collaboration experience with cross-functional teams for product development.

Responsibilities

  • Develop ML models for detecting various types of fraud.
  • Design graph-based systems for fraud detection including GNN models.
  • Utilize Generative AI for improving detection and automating workflows.
  • Collaborate with stakeholders to translate fraud issues into data solutions.
  • Create and scale highly-effective ML models and algorithms.
  • Implement model pipeline with engineering teams to deploy services.
  • Conduct root cause analysis for system issues.

Benefits

  • Collaborative work environment with cross-functional teams.
  • Opportunities for impacting fraud detection strategies.
  • Access to advanced technology and innovative AI techniques.
  • Potential for career growth within a leading retail organization.
Full Job Description
Position Summary...

What you'll do...
We are looking for a Senior Data Scientist to join Sam's Club fraud detection team. As a Senior Data Scientist, you will be responsible for owning fraud risks in various product segments and being a strategic partner to product & business teams. You will be tasked to set goals, create strategy, and closely collaborate with product managers, engineers and business stakeholders. You'll be responsible for detecting changed fraud trends, building and implementing ML models with high-dimensional, fast moving real time dataset and driving innovation in detecting & preventing fraud across various channels. You will also help shape the next generation of fraud intelligence by leveraging Graph Neural Networks (GNNs), network-based modeling, and Generative AI (GenAI) techniques to enhance detection accuracy, accelerate investigations, and improve model explainability and decision intelligence.

**Immigration Sponsorship support will NOT be available for this
position**

What You'll Do:
  • Use machine learning to develop models for fraud detection in areas such as E-Commerce & In-club payment fraud, Return abuse, Account takeover (ATO) etc.
  • Design and implement graph-based fraud detection systems, including link analysis, entity resolution, and Graph Neural Network (GNN) models to detect coordinated fraud rings and network-level risk signals.
  • Leverage Generative AI techniques (e.g., LLMs and Agentic workflows) to enhance fraud detection, automate investigation workflows, generate risk narratives, and improve model explainability.
  • Partnering with business and technical stakeholders to translate fraud business problems into data science solutions.
  • Work on highly-scalable ML models and algorithms in big data mining, graph modeling & other domains.
  • Work with engineering teams to implement model pipeline and deploy the service at scale.
  • Swiftly respond to system issues and deep dive into root cause analysis


What You'll Bring:
  • Industry experience in building production machine learning systems at scale.
  • 5-7 years of experience with languages used to manipulate data and draw insights from large data sets (e.g. Python, SQL, etc.)
  • Experience working with large data sets and distributed computing tools (PySpark/GCP/BigQuery).
  • Hands-on experience or strong familiarity with Graph Neural Networks (GNNs), graph embeddings, or large-scale graph processing frameworks.
  • Experience exploring or applying Generative AI / LLM-based approaches for decision intelligence, feature generation, workflow automation, or explainability.
  • Experience in fraud risk solutions is desirable
  • Experience in working with cross-functional product and engineering teams to understand requirements and incorporate them in the roadmap.


Minimum Qualifications...

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

Option 1- Bachelor's degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or related field and 3 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 1 years' experience in an analytics related field. Option 3 - 5 years' experience in an analytics or related field.

Preferred Qualifications...

Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications.

Data science, machine learning, optimization models, Master's degree in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch), We value candidates with a background in creating inclusive digital experiences, demonstrating knowledge in implementing Web Content Accessibility Guidelines (WCAG) 2.2 AA standards, assistive technologies, and integrating digital accessibility seamlessly. The ideal candidate would have knowledge of accessibility best practices and join us as we continue to create accessible products and services following Walmart's accessibility standards and guidelines for supporting an inclusive culture.

Primary Location...

2101 Se Simple Savings Dr, Bentonville, AR 72712-4304, United States of America

Walmart and its subsidiaries are committed to maintaining a drug-free workplace and has a no tolerance policy regarding the use of illegal drugs and alcohol on the job. This policy applies to all employees and aims to create a safe and productive work environment.

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