Principal, Data Scientist

Position Summary...

What you'll do...

It's an exciting time to join Walmart's journey toward building intelligent, AI-powered platforms that transform how we identify risk, improve supplier experience, and drive data-driven decision making at enterprise scale. The Finance Retail & Audit Analytics (FRAA) organization is investing heavily in next-generation AI, Machine Learning, and Data Science capabilities that enable anomaly detection, predictive insights, intelligent automation, and scalable audit intelligence solutions.

About Team:

The FRAA team is responsible for building intelligent analytics products that help identify risk signals, reduce supplier friction, automate audit processes, and provide predictive decision support across Walmart's global ecosystem. Our vision is to create an enterprise-grade AI platform that combines machine learning, advanced analytics, GenAI, and scalable data engineering to proactively surface insights and drive measurable business outcomes. As a Principal / Staff Data Scientist, you will play a critical role in shaping the technical vision, architecture, and delivery of AI-powered products that support the future of FRAA.

You will work closely with engineering, product, analytics, audit, and business teams to operationalize machine learning solutions at scale and drive the adoption of AI-first decision-making across the organization.

What you'll do:

* Lead the AI/ML strategy and technical direction for next-generation FRAA platforms focused on anomaly detection, predictive analytics, supplier intelligence, and audit automation.

* Design, develop, and deploy scalable machine learning models and AI solutions that solve complex business and risk management challenges.

* Build and operationalize advanced analytics capabilities including classification, regression, clustering, anomaly detection, forecasting, and recommendation systems.

* Develop intelligent anomaly detection frameworks leveraging techniques such as Isolation Forest, Random Forest, statistical methods, and unsupervised learning algorithms.

* Partner with business stakeholders to translate audit, compliance, supplier, and operational challenges into measurable AI/ML solutions.

* Build end-to-end machine learning pipelines including feature engineering, model training, experimentation, validation, deployment, monitoring, retraining, and optimization.

* Develop scalable predictive models that proactively identify risks, exceptions, opportunities, and emerging business trends across large enterprise datasets.

* Leverage semantic search, vector databases, embeddings, NLP, LLMs, and Generative AI technologies to build intelligent audit and decision-support systems.

* Architect enterprise-grade AI solutions using cloud-native technologies, APIs, microservices, Docker, Kubernetes, and CI/CD deployment frameworks.

* Collaborate with data engineering teams to design scalable data architectures, feature stores, and ML-ready data products.

* Work with large-scale distributed data processing frameworks including Spark, Big Query, DBT, and cloud-native analytical platforms.

* Establish machine learning governance, model monitoring, explainability, and responsible AI best practices.

* Drive technical innovation through research, experimentation, and evaluation of emerging AI and machine learning technologies.

* Mentor and develop data scientists, machine learning engineers, and analytics teams while fostering a culture of innovation and technical excellence.

* Influence organizational AI strategy, roadmap development, and platform adoption through strong cross-functional leadership and executive communication.

* Ensure business needs are being met by evaluating the effectiveness of AI solutions, measuring business impact, and continuously improving model performance and operational efficiency.

* Promote and support company policies, procedures, mission, values, and standards of ethics and integrity while driving responsible and scalable AI adoption.

What you'll bring:

* Advanced experience designing, building, and deploying machine learning solutions in production environments at enterprise scale.

* Strong expertise in Python and modern machine learning frameworks such as Scikit-Learn, Tensor Flow, PyTorch, XGBoost, or similar technologies.

* Deep experience developing machine learning models including:

* Random Forest

* Isolation Forest

* Classification Models

* Regression Models

* Clustering Algorithms

* Anomaly Detection Frameworks

* Predictive Analytics and Forecasting Models

* Proven track record operationalizing AI/ML solutions from experimentation through production deployment and monitoring.

* Strong understanding of feature engineering, model evaluation, model explainability, and MLOps best practices.

* Experience building scalable ML pipelines and workflows using orchestration frameworks such as Airflow, Kubeflow, MLFlow, or similar platforms.

* Strong data engineering foundations including SQL, data modeling, ETL/ELT design, and distributed data…