Senior Data Scientist - Generative BI

About the role

The Home Depot is looking for a Senior Data Scientist - Generative BI to drive business profitability and modernized analytics across the company. The role focuses on practical AI enablement rather than building generative models from scratch: turning user feedback and analytical friction into robust technical solutions. It sits in the Store Support Center and reports to a manager or above, with no direct reports.

Key responsibilities

• Build and maintain rich semantic layers, curate golden SQL query examples, set up automated regression testing pipelines for AI-generated answers, and integrate new enterprise data sources into a scalable multi-agent architecture.
• Design and develop algorithms and models on large datasets, interpret advanced analytical methods, and communicate insights and recommendations to technical and non-technical leaders.
• Work with project teams and business partners to define goals, prioritize work, mentor junior colleagues, distribute workload, and support recruiting.
• Build trust with internal customers and cross-functional teams, educate them on advanced analytics, and look for new business opportunities where data science is a competitive advantage.
• Explore developments in data science, document reusable algorithms and codified data products, and define best practices for model production.

Required profile

• Ages 18 or older and legally permitted to work in the United States.
• Bachelor's degree or equivalent in a field related to the job.
• Preferred: PhD in a quantitative field (Computer Science, Math, Statistics, etc.).
• Preferred: 6+ years of experience in business intelligence and analytics.

Required skills

• Preferred: modern scripting language (preferably Python).
• Preferred: querying data (preferably with Google BigQuery or SQL).
• Preferred: generative AI chatbots, large language models or conversational analytics.
• Preferred: Databricks ecosystem.
• Preferred: statistical techniques, predictive modeling, data mining and analysis.
• Preferred: GitHub repositories and standard CI/CD practices.