Data Scientist III - Fraud Analytics & Machine Learning

About the Role

Every day, millions of customers trust Walmart to deliver a seamless shopping experience. As a Data Scientist III on the Inkiru Data Science team, you'll transform billions of data points into intelligent solutions that identify fraud, reduce risk, and protect both customers and the business. You'll partner with engineers, product managers, and business leaders to build analytics and machine learning solutions that directly influence decisions at global scale.

Why This Role Matters

Fraud evolves every day—and so do we. Our team combines advanced analytics, statistical modeling, machine learning, and AI to stay ahead of emerging threats while delivering a seamless experience for customers and associates. Your work will power real products, influence strategic decisions, and help protect one of the largest retailers in the world.

Responsibilities

• Detect emerging fraud trends using large-scale behavioral, transactional, and operational data.
• Build analytical models that improve fraud detection, risk management, and business decision-making.
• Create intuitive dashboards and visualizations that help leaders quickly understand key business metrics.
• Partner closely with Product, Engineering, Data Science, and Business teams to bring analytical solutions into production.
• Explore new analytical techniques—including machine learning and AI—to solve evolving business challenges.
• Improve data quality through validation, governance, and scalable analytical processes.
• Communicate complex findings through compelling storytelling that drives action.

Technologies You'll Work With

• Python
• SQL
• Spark
• Hive
• BigQuery
• Distributed computing platforms
• Tableau
• Power BI
• Machine Learning frameworks
• Cloud-scale analytics platforms
• AI-assisted analytics and modern data science workflows

Team Culture

• Solve problems at one of the largest scales in retail.
• Work with experienced data scientists and machine learning engineers.
• Influence product strategy with data-driven insights.
• Build solutions that move from concept into production.
• Learn new technologies while working on meaningful business challenges.
• Grow your career through mentorship, leadership opportunities, and cross-functional collaboration.

What Success Looks Like

Within your first year, you'll:

• Deliver analytical solutions that influence fraud strategy.
• Build trusted relationships across Product, Engineering, and Business teams.
• Create scalable dashboards and reporting used by leadership.
• Improve fraud detection through innovative analytical approaches.
• Contribute to production-ready data science solutions that create measurable business value.

Requirements

• Strong analytical and statistical problem-solving skills.
• Experience working with large, complex datasets.
• Proficiency in SQL and Python (R is also welcome).
• Experience with distributed computing technologies such as Spark, Hive, or BigQuery.
• Familiarity with data visualization tools including Tableau, Power BI, or Looker.
• Ability to communicate technical concepts clearly to both technical and non-technical audiences.
• Passion for solving ambiguous problems using data.

Qualifications

Minimum qualifications:

• Option 1: Bachelor's degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 2 years' experience in an analytics or related field.
• Option 2: Master's degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field.
• Option 3: 4 years' experience in an analytics or related field.

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 (e.g., scikit-learn, TensorFlow, PyTorch).
• 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.

Bonus Points

• Experience in fraud analytics, financial risk, cybersecurity, or trust and safety.
• Experience building or deploying machine learning models.
• Knowledge of experimentation, causal inference, or advanced statistical techniques.
• Experience with Generative AI, LLMs, or AI-assisted analytics.
• Experience working in cloud-native data environments.

Location

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