Data Scientist - Space Intelligence

The Home Depot; Atlanta (GA); On-site; USD 120,000 - 180,000; Full time

Job summary

The Home Depot is seeking a Data Scientist to advance AI-powered merchandising solutions. You will build scalable deep learning models, computer vision, knowledge graphs, and agentic AI in production, collaborating with product managers, engineers, and stakeholders to deliver reliable decisions that improve efficiency and customer experience.

This role focuses on lifecycle AI—from problem framing to deployment and ongoing monitoring—while communicating insights to both technical and

Qualifications

• Bachelor's degree in a quantitative field or related area.
• Experience deploying ML models to production environments.
• Strong knowledge of deep learning, CV, and ML lifecycle.

Responsibilities

• Design and develop DL/ML models for large datasets to derive business insights.
• Collaborate with product managers and engineers to productionize models.
• Monitor and improve models post-deployment and communicate results clearly.
• Explore new AI methods and build reusable analytics components.

Skills

Python

Deep learning

Computer vision

GenAI/agentic AI

LangChain

Cloud deployment

GNNs/knowledge graphs

PyTorch/TensorFlow

Production ML systems

Education

Bachelor's degree

Tools

Vertex AI

Kubernetes

Docker

Graph databases

Python ( coding )

Job description

Position Purpose

We are transforming how merchandising decisions are made through AI-powered solutions and automation. This Data Scientist plays a key role in building scalable deep learning, computer vision, knowledge graph, and agentic AI solutions in production that directly impact retail and merchandising by solving complex, high-value business problems. The position focuses on science-driven models, operating at the intersection of data science, GenAI/agentic systems, and cloud deployment to deliver reliable decision systems that increase efficiency and improve customer experience.

Job Description

Position Purpose

We are transforming how merchandising decisions are made through AI-powered solutions and automation. This Data Scientist plays a key role in building scalable deep learning, computer vision, knowledge graph, and agentic AI solutions in production that directly impact retail and merchandising by solving complex, high-value business problems. The position focuses on science-driven models, operating at the intersection of data science, GenAI/agentic systems, and cloud deployment to deliver reliable decision systems that increase efficiency and improve customer experience.

This role contributes to AI initiatives across the full lifecycle, from business problem framing through technical design, development, deployment, and ongoing monitoring and enhancement. The Data Scientist partners closely with product managers, software engineers, and business stakeholders to identify high-impact opportunities, translate them into technical requirements, and deliver scalable solutions in production. The role is responsible for communicating insights and recommendations to both technical and non-technical audiences and ensuring models perform effectively based on real-world outcomes.

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, Data Science, Analytics, Engineering or other quantitative fields.
• 4+ years of hands‑on experience (or 2 years with PhD), developing and deploying machine learning or deep learning models into production environments, integrating them into scalable data pipelines and decision systems (e.g., via APIs, microservices, or batch pipelines), ensuring reliability, scalability, traceability, and interoperability.
• Proficient in deep learning frameworks (e.g., PyTorch, TensorFlow) and modern programming languages (Python, etc.) to prototype and productionize solutions.
• Strong experience with computer vision techniques (e.g., object detection, image segmentation, OCR) and applying them to real-world production use cases.
• Strong experience with GenAI, LLMs, and agentic frameworks (e.g., LangChain, LangGraph), including designing multi‑agent orchestration, and RAG pipelines for production use cases.
• Strong understanding of knowledge graph construction and reasoning (e.g., graph embeddings, GNNs) and experience leveraging graph databases and/or graph query languages to build and query large‑scale knowledge graphs.
• Strong understanding of modern cloud ecosystems (GCP, AWS, etc.) and experience deploying ML/AI workloads using cloud‑native services (e.g., Vertex AI, containerization/Kubernetes).
• Familiarity with optimization solvers (e.g., Gurobi, CPLEX, OR-Tools) and modern programming languages (Python, etc.) to prototype and productionize solutions.
• Comfortable collaborating with front‑end developers or building light UI prototypes (e.g., Streamlit, React).
• Experience applying deep learning, graph‑based, and agentic AI approaches to supply chain, logistics, retail or merchandising problems such as elasticity modeling, product relationship modeling, and demand forecasting.

Minimum Education

• The knowledge, skills and abilities typically acquired through the completion of a bachelor's degree program or equivalent degree in a field of study related to the job.

Preferred Education

• No additional education

Minimum Years Of Work Experience

• 3

Preferred Years Of Work Experience

• No additional years of experience

Minimum Leadership Experience

• None

Preferred Leadership Experience

• None

Certifications

• None

Competencies

• Action Oriented: Taking on new opportunities and tough challenges with a sense of urgency, high energy, and enthusiasm
• Business Insight: Applying knowledge of the business and the marketplace to advance the organization's goals
• Collaborates: Building partnerships and working collaboratively with others to meet shared objectives
• Communicates Effectively: Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences
• Customer Focus: Building strong customer relationships and delivering customer-centric solutions
• Drives Results: Consistently achieving results, even under tough circumstances
• Nimble Learning: Actively learning through experimentation when tackling new problems, using both successes and failures as learning fodder
• Optimizes Work Processes: Knowing the most efficient and effective processes to get things done, with a focus on continuous improvement
• Plans and Aligns: Planning and prioritizing work to meet commitments aligned with organizational goals
• Self-Development: Actively seeking new ways to grow and be challenged using both formal and informal development channels