(USA) Senior Manager, Data Science

Position Summary...

What you'll do...

The Senior Manager, Data Science in E2E Product Excellence team will be a key contributor in AI discovery, research, algorithmic solution design, and rapid prototyping of ML solutions to enhance efficiency across Walmart's supply chain operations.

This role will focus on designing ML and OR models that unify supply chain signals to improve inventory placement, optimize fulfillment and capacity planning. The Senior Manager will be responsible for detailed ML/AI model design, feature engineering, model development and experimentation, and pilot testing with iterative refinement. Success in this role will require close partnership with cross-functional teams, including data governance, engineering, product, business and operations.

What you'll do:

• Understanding Business Context: Requires knowledge of industry factors, business practices across domains like product, supply chain, and retail, and key business metrics. Translates business needs into projects aligned with strategy, develops domain-specific artifacts, and connects business needs to actionable solutions. Provides insights and recommendations to address complex issues and drive ROI or cost savings.

• Tech. Problem Formulation: Requires knowledge of analytics, big data, automation, and business insights. Translates business problems into data-driven solutions, identifies suitable methods and tools, and shares use cases to demonstrate their effectiveness.

• Data Source Identification: Requires knowledge of business domains, data categories, storage systems (SQL/NoSQL), and data quality. Identifies suitable data sources, prioritizes requirements, ensures alignment with SLAs, and performs initial data quality checks.

• Data Strategy: Requires knowledge of data-driven decision-making, data management, governance, quality standards, and scalability. Applies strategic principles to solve routine business problems and unlock the value of data assets.

• Code Development and Testing: Requires knowledge of coding languages (e.g., SQL, Java, Python) and testing methods (static, dynamic, manual). Develops solutions by writing code based on business and technical requirements and tests using appropriate approaches.

• Analytical Modeling: Requires expertise in feature selection, exploratory data analysis methods, and advanced statistical techniques, including graphical models, Bayesian inference, neural networks, support vector machines (SVM), random forests, basic natural language processing (NLP), and vision models. Proficiency in multivariate calculus, optimization methods (Newton-Raphson, gradient descent), and numerical optimization techniques (linear, integer, and quadratic programming) is essential. Uses R/Python to develop custom models for complex data. Conducts statistical analysis, hypothesis testing, and clustering to derive insights. Designs experiments, refines features, and mentors junior associates on advanced modeling techniques.

• Model Deployment and Scaling: Requires knowledge of how variables and features impact model performance, as well as an understanding of servers …