This role leads the team’s advanced analytics efforts and is the primary owner of the Omni Assortment Insights (OmniAI) model. The individual combines deep technical expertise with strategic oversight, ensuring the OmniAI engine’s development aligns with business goals while also driving the adoption of these AI-driven practices within the enterprise. What you'll do... OmniAI Model Leadership: Own the design, development, and refinement of the OmniAI assortment optimization model. This includes overseeing the model’s logic for store-level assortment decisions, validating its performance, and guiding enhancements (e.g. integrating new data sources, improving algorithms for better demand prediction and space-aware optimization). The role is accountable for the model’s accuracy, scalability, and outcomes. Innovation & Continuous Improvement: Keep pushing the boundaries of what the OmniAI model and related agents can do. Evaluate and incorporate advanced analytical techniques (such as machine learning, optimization methods, or even LLM-driven interfaces for merchants) to improve the system’s confidence and autonomy. The Advanced Analytics owner might prototype new features – for example, a conversational interface for merchant inputs or an improved algorithm for regional diversity – and work with the team to productionize these innovations. Bridge Between Data Science and Business: Serve as the subject matter expert who can translate complex model outputs into actionable insights for the business. This means explaining the “why” behind the AI recommendations in clear terms, helping merchants and leaders understand how decisions were arrived at. By providing transparency into the model’s drivers (e.g. emphasizing customer trend signals or supplier performance data behind a recommendation), build trust in the system’s decisions and ease the team’s reliance on the traditional way of working. Champion Adoption and Culture Change: Champion the integration of OmniAI and continuous assortment processes within the merchandising organization. This involves coordinating training sessions, creating documentation or playbooks, and being the go-to resource for team members as they transition to the new tools. The person in this role tactfully leads stakeholders through process changes – for example, helping merchants adapt from annual planning to a continual optimization mindset – without formally labeling it as “change management,” but by demonstrating the benefits and supporting the team through the transition. Cross-Functional Coordination: Work with leaders in merchandising, product management, engineering, and operations to ensure the AI-driven assortment engine is supported and delivering value. For example, align with IT on system capabilities needed, with product teams on user interface requirements for the merchant-facing parts of OmniAI, and with operations on how the continual changes are implemented in stores. By keeping all parties in sync, this role ensures that technical advances translate into practical business results. Measure Impact and Guide Strategy: Use advanced analytics to measure the impact of the continual merchandising approach (e.g. lift in sales, reduction in stock-outs, improved local relevance metrics). Communicate these insights to leadership and use them to guide the team’s priorities. If certain categories or metrics are lagging, the Advanced Analytics owner investigates the data, diagnoses issues (perhaps the model needs more data on a new trend or an agent needs re-calibration), and steers the team to address them. This data-driven oversight ensures the project stays focused on high-value improvements and achieves Walmart’s strategic goals for assortment. Technical Strategy & Alignment: Set the technical direction for the team’s analytics approach, making sure that the efforts of agent development align with the overall model architecture and Walmart’s broader data strategy. For instance, ensure that the forecasting and optimization techniques used by planning agents are consistent with the model’s methodology, and that execution constraints identified in the field inform future model updates.
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Job Type
Full-time
Career Level
Mid Level
Number of Employees
5,001-10,000 employees