Data Scientist

With a career at The Home Depot, you can be yourself and also be part of something bigger.

Position Purpose:

The Data Scientist will be a key architect in building next-generation agentic systems designed to revolutionize how professional customers manage complex projects. This role is responsible for developing autonomous AI capabilities that bridge the gap between simple search and sophisticated project quoting and fulfillment. You will leverage advanced techniques in Conversational AI, Retrieval-Augmented Generation (RAG), and Multimodal AI to unlock vast enterprise catalogs, automate project and product configuration, and provide real-time sourcing across a massive enterprise assortment.

As a member of the team, you will design the reasoning paths that allow AI agents to navigate "Pro-speak" queries, extract intent from multimodal project inputs, and manage complex state across omnichannel sessions. Your work will directly empower Pro customers to move from discovery to optimized quoting to transaction in minutes, providing a seamless, self-serve experience at scale.

Key Responsibilities:

• 55% Solution Development - Design and develop algorithms and models to use against large datasets to create business insights; Participates in large data analytics project teams by serving as a technical lead for analytics projects; May lead small projects and work independently on solution development; Execute tasks with high levels of efficiency and quality; Make appropriate selection, utilization and interpretation of advanced analytical methodologies
• 20% Communicating Results - Effectively communicate insights and recommendations to both technical and non-technical leaders and business customers/partners; Present recommendations in a confident manner in order to influence execution of recommendation; Prepare reports, updates and/or presentations related to progress made on a project or solution; Clearly communicate impacts of recommendations to drive alignment and appropriate implementation
• 10% Business Collaboration - Incorporate business knowledge into solution approach; Effectively develop trust and collaboration with internal customers and cross-functional teams; Work with project teams and business partners to determine project goals
• 15% Technical Exploration & Development - Seek further knowledge on key developments within data science, technical skill sets, and additional data sources; Participate in the continuous improvement of data science and analytics by developing replicable solutions (for example, codified data products, project documentation, process flowcharts) to ensure solutions are leveraged for future projects; Build and maintain library of reusable algorithms for future use, ensuring developed codes are documented

Direct Manager/Direct Reports:

• This position typically reports to Manager or above
• This position has 0 Direct Reports

Travel Requirements:

• Typically requires overnight travel less than 10% of the time.

Physical Requirements:

• Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles.

Working Conditions:

• Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.

Minimum Qualifications:

• Must be eighteen years of age or older.
• Must be legally permitted to work in the United States.

Preferred Qualifications:

• Masters in a quantitative field (Computer Science, Math, Statistics, etc.) or equivalent work experience

• AI Orchestration: 4+ years of experience in Data Science with a focus on Conversational AI, GenAI, agentic workflows, custom tool-calling, and reasoning traces (e.g., LangSmith).

• Advanced Retrieval: Mastery of RAG and vector database architectures, specifically for extracting technical specs from unstructured enterprise data.

• Multimodal AI: Experience building pipelines to extract structured SKU-level intent from unstructured multimodal inputs, such as photos or handwritten lists.

• Algorithmic Logic: Background in similarity scoring and attribute-matching to resolve vague technical queries.

• State & Engineering: Experience building stateful AI applications that maintain context across devices and sessions.

• Domain Expertise: Prior experience in B2B e-commerce, supply chain, or trade-related data is a significant p