AI Applications Analyst (Walmart)

Retail Insight•Bentonville, AR

28d•Onsite

About The Position

At Retail Insight (RI), we transform data into actionable strategies, empowering retailers and CPGs to make smarter decisions. Our cutting-edge algorithms and innovative retail execution products are trusted by many of the world’s leading companies to improve sales, profitability, and operational efficiency. From tackling out-of-stocks and poor in-store execution to reducing waste, markdowns, and shrink, RI helps businesses unlock performance drivers through advanced analytics. We’re looking for someone who is curious, creative, and technically sharp to step into a new role as an AI Applications Analyst to help us build the next generation of internal tools. This is a hands-on role where you’ll blend Python development, AI integration, and product thinking to design smart, scalable solutions that make work easier, faster, and more insightful for teams across the business. If you’ve ever wanted to build apps that feel magical to use — this is your chance.

Requirements

Excellent SQL skills and knowledge of relational data models.
Experience building or supporting internal tools or dashboards (Streamlit, Power BI, Tableau, etc.).
Familiarity with concepts in machine learning, LLMs, or conversational interfaces.
Clear communicator, able to work across teams.
Naturally curious, quick to learn new tools and tech.

Nice To Haves

Python scripting or app development experience.
Hands-on with Streamlit, Chainlit, or similar frameworks.
Knowledge of Google ADK, Vertex AI, or LLM orchestration.
Understanding of RAG pipelines and agent workflows.
Experience with dbt, LookML, or modular data modelling.
Track record of building self-serve analytics tools or automating insights.

Responsibilities

Build AI-powered tools that help teams access insights, automate workflows, and interact with data in natural language.
Use frameworks like Streamlit, Chainlit, and Google ADK to create apps that combine data, visualisation, and AI in user-friendly interfaces.
Integrate Retrieval-Augmented Generation (RAG) and intelligent agents to deliver smarter, context-aware outputs.
Develop self-serve analytics tools so teams can answer their own business questions without heavy analyst support.
Automate recurring insight workflows using SQL, dbt, and embedded analytics.
Monitor, refine, and improve AI performance through structured feedback loops, robust logging, and prompt optimisation.
Collaborate with product, engineering, analytics, and operations partners to identify high-impact use cases and turn them into tools people love to use.