Principal, Data Scientist

Position Summary...

About the Role

We are looking for a Principal Data Scientist to lead high-impact initiatives within Walmart's Decision Science organization. This role is centered on building scalable, end-to-end Data Science and AI solutions that improve customer experience, optimize operational and sourcing decisions, and drive measurable business impact across the enterprise.

As a senior technical leader, you will partner closely with business, product, engineering, and operations teams throughout the entire lifecycle of a solution - from problem formulation and opportunity identification to model development, experimentation, deployment, and impact measurement. You will help define success metrics, design rigorous experiments, develop scalable AI/ML systems, and influence strategic decision-making through data-driven insights and innovation.

Our team is deeply customer-focused and works on some of the most critical challenges in retail and supply chain, building intelligent systems that improve item availability, enable smarter sourcing and inventory decisions, optimize operational efficiency, and ultimately enhance the end-to-end customer experience. We are investing heavily in GenAI-powered capabilities that improve business insights, accelerate decision-making, and enhance stakeholder productivity through intelligent analytics, conversational experiences, and AI-assisted workflows.

You will play a key role in shaping the long-term data science and AI vision of Walmart, driving innovation across both traditional ML and GenAI applications, and raising the technical bar across the organization.

What you'll do...

What You'll Do

Strategic Leadership

* Define and drive the Data science roadmap aligned with Decision Science priorities.

* Partner closely with business, product, engineering, and operations teams to identify high-impact opportunities and translate them into scalable ML and AI solutions.

* Influence executive stakeholders through data-driven insights, strategic recommendations, and thought leadership.

* Translate ambiguous business challenges into structured analytical frameworks, measurable success metrics, and actionable roadmaps.

* Drive end-to-end ownership from problem formulation through experimentation, deployment, and post-launch impact measurement.

Technical Excellence

* Design, develop, and deploy end-to-end ML and data science products, including problem framing, feature engineering, model development, experimentation, deployment, and measurement on business metrics.

* Develop and deploy production-grade GenAI solutions using modern LLMs and agent orchestration frameworks, including prompt engineering, tool integration, skills and retrieval-based workflows, to improve business insights, automate operational workflows, and enhance decision-making.

* Define and track meaningful success metrics aligned with customer experience and operational goals.

* Develop scalable pipelines and production-ready solutions in partnership with engineering teams.

* Ensure model quality, explainability, monitoring, and operational excellence.

Qualifications

* Master's degree or PhD in Computer Science, Statistics, Mathematics, Economics, Operations Research, Data Science, or a related quantitative field.

* 10+ years of experience in Data Science, Machine Learning, Advanced Analytics, or related disciplines, with a proven track record of leading large-scale, high-impact initiatives.

* Strong expertise in statistical modeling, machine learning, experimentation, causal inference, and data science methodologies.

* Experience building and deploying scalable machine learning solutions and AI-powered applications in production environments.

* Hands-on experience developing GenAI solutions using Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG), Skills, vector databases, and agentic workflows.

* Experience with cloud AI/ML platforms and modern AI tooling ecosystems, preferably on GCP or Azure.

* Experience with distributed data processing frameworks such as Spark and large-scale data platforms.

* Experience with MLOps and LLMOps practices, including feature stores, model…