Sr Data Scientist - AI Engineer

Home Depot / THD; Atlanta (GA); On-site; USD 130,000 - 190,000; Full time

Job summary

The Home Depot's THD data science team is seeking a Senior Data Scientist to build production-ready AI capabilities and establish MLOps/LLMOps/AIOps frameworks that connect enterprise data to real workflows in Atlanta.

You will own solution development across analytics, design models, and translate results into actionable guidance for technical and non-technical leaders, while mentoring teammates and helping recruit talent.

Qualifications

• Minimum 6+ years of experience in data science, machine learning engineering, AI engineering, software engineering, or MLOps, focused on production-ready AI solutions
• 2+ years of hands‑on experience developing, deploying, evaluating, or supporting GenAI, LLM‑based, or agentic AI solutions
• Experience building agentic AI systems and multi‑step workflows using tool calling, reasoning and planning, state and memory management, structured outputs, RAG/retrieval systems, embeddings, and API integration
• Strong software engineering skills: Python, SQL, automated testing, containerization (Docker/Kubernetes), and cloud deployment (GCP preferred), with collaboration across Engineering, DevOps, and SRE partners
• Expertise in foundational MLOps/LLMOps/AIOps practices: CI/CD automation, model/agent registries, versioning, automated testing, monitoring, automated retraining, rollback strategies, release management, and production support
• Expertise in AI observability and governance: tracing, telemetry, automated alerting, anomaly detection, incident triage, eval harnesses, safety guardrails, model explainability, approval paths, fallback mechanisms, tool‑use auditing, and cost/latency monitoring, including human‑in‑the‑loop controls
• Hands‑on experience with agent orchestration frameworks, structured agent communication protocols (MCP, A2A), and Infrastructure‑as‑Code (IaC)
• Ability to prototype lightweight tools or interfaces, evaluate technical feasibility, and document reusable architectural patterns
• Continuous learning agility for emerging AI architectures, protocols, and operating models
• Domain experience in merchandising, retail, ecommerce, supply chain, assortment planning, or space planning

Responsibilities

• Design and develop algorithms and models that generate business insights from large datasets
• Apply advanced analytical methodologies, including appropriate selection, utilization, and interpretation
• Communicate insights and recommendation impacts to drive alignment and implementation
• Prepare reports, updates, and presentations on progress for projects and solutions
• Mentor and coach more junior roles to strengthen technical competencies
• Collaborate with managers and teams on distribution of workload and resources
• Support recruiting and hiring efforts for the team
• Leverage business knowledge to shape the solution approach
• Develop trust and collaboration with internal customers and cross-functional teams
• Educate technical and non-technical partners on advanced analytics
• Apply a deep understanding of IT needs to help the team tackle business problems
• Actively pursue new business opportunities where data science can drive competitive advantage
• Stay current on key data science developments, technical skill sets, and additional data sources
• Improve data science and analytics through replicable solutions for future reuse
• Define best practices and develop a clear vision for productionalization
• Contribute to a library of reusable algorithms with documented code

Skills

Python

SQL

Docker

Kubernetes

GCP

CI/CD

RAG

Embeddings

API integration

MLOps

LLMOps

AIOps

IaC

MCP

A2A

Agentic AI

Education

Bachelor’s degree or higher in a related field

Tools

Python

SQL

Docker

Kubernetes

GCP

CI/CD

MLOps

LLMOps

AIOps

IaC

MCP

A2A

Embeddings

RAG

API integration

Job description

Home Depot’s THD data science team is looking for a Senior Data Scientist to build production-ready AI capabilities that connect enterprise data to real workflows. This role focuses on establishing MLOps/LLMOps/AIOps operational frameworks and developing agentic AI solutions that can reason over data and orchestrate models into end-to-end execution.

What you will do

You will take ownership of solution development across advanced analytics, from designing models for large datasets to selecting and applying analytical methodologies that support business outcomes. A major part of the work includes ensuring outputs are executed with strong efficiency and quality, then translating results into clear recommendations for technical and non-technical leaders.

• Design and develop algorithms and models that generate business insights from large datasets
• Apply advanced analytical methodologies, including appropriate selection, utilization, and interpretation
• Communicate insights and recommendation impacts to drive alignment and implementation
• Prepare reports, updates, and presentations on progress for projects and solutions

You will also collaborate across teams to shape project direction and execution. This includes helping set goals, guiding prioritization, and supporting team quality through direction and coaching of more junior roles. The role includes coordination with managers and teams on workload and resource distribution, and involvement in recruiting and hiring efforts for the group.

• Work with project teams and business partners to determine project goals
• Provide direction on prioritization and ensure quality of work
• Mentor and coach more junior roles to strengthen technical competencies
• Collaborate with managers and teams on distribution of workload and resources
• Support recruiting and hiring efforts for the team

Additional responsibilities include leveraging business context to improve solution approaches and building trusted relationships with internal customers and cross-functional teams. You will also support partner enablement by providing general education on advanced analytics, aligned with your understanding of IT needs required to solve business problems and create data science advantage.

• Leverage business knowledge to shape the solution approach
• Develop trust and collaboration with internal customers and cross-functional teams
• Educate technical and non-technical partners on advanced analytics
• Apply a deep understanding of IT needs to help the team tackle business problems
• Actively pursue new business opportunities where data science can drive competitive advantage

On the technical side, you will continue exploring emerging capabilities and improving how solutions are delivered. This includes developing replicable, well-documented solutions (such as codified data products, project documentation, and process flowcharts), defining best practices for data analysis and model productionalization, and contributing to a reusable library of algorithms with clear documentation.

• Stay current on key data science developments, technical skill sets, and additional data sources
• Improve data science and analytics through replicable solutions for future reuse
• Define best practices and develop a clear vision for productionalization
• Contribute to a library of reusable algorithms with documented code

Requirements

• Must be eighteen years of age or older
• Must be legally permitted to work in the United States
• Minimum 6+ years of experience in data science, machine learning engineering, AI engineering, software engineering, or MLOps, focused on production-ready AI solutions
• 2+ years of hands‑on experience developing, deploying, evaluating, or supporting GenAI, LLM‑based, or agentic AI solutions
• Experience building agentic AI systems and multi‑step workflows using tool calling, reasoning and planning, state and memory management, structured outputs, RAG/retrieval systems, embeddings, and API integration
• Strong software engineering skills: Python, SQL, automated testing, containerization (Docker/Kubernetes), and cloud deployment (GCP preferred), with collaboration across Engineering, DevOps, and SRE partners
• Expertise in foundational MLOps/LLMOps/AIOps practices: CI/CD automation, model/agent registries, versioning, automated testing, monitoring, automated retraining, rollback strategies, release management, and production support
• Expertise in AI observability and governance: tracing, telemetry, automated alerting, anomaly detection, incident triage, eval harnesses, safety guardrails, model explainability, approval paths, fallback mechanisms, tool‑use auditing, and cost/latency monitoring, including human‑in‑the‑loop controls
• Hands‑on experience with agent orchestration frameworks, structured agent communication protocols (MCP, A2A), and Infrastructure‑as‑Code (IaC)
• Ability to prototype lightweight tools or interfaces, evaluate technical feasibility, and document reusable architectural patterns
• Continuous learning agility for emerging AI architectures, protocols, and operating models
• Domain experience in merchandising, retail, ecommerce, supply chain, assortment planning, or space planning

Tools and technologies

• Python, SQL
• Docker, Kubernetes
• GCP
• CI/CD, CI/CD automation
• RAG, retrieval systems, embeddings
• API integration
• MLOps, LLMOps, AIOps
• IaC, MCP, A2A

Role details

• Location: Atlanta, GA (onsite)
• Minimum years of work experience: 5
• Travel: typically requires overnight travel less than 10% of the time
• Education: bachelor’s degree program or equivalent degree in a field of study related to the job
• Reporting: this position reports to manager or above; 0 direct reports

Working conditions

• Most of the time is spent sitting in a comfortable position, with frequent opportunity to move about
• On rare occasions, there may be a need to move or lift light articles
• Located in a comfortable indoor area; any unpleasant conditions would be infrequent and not objectionable

Competencies

• Attracts Top Talent
• Business Insight
• Collaborates
• Communicates Effectively
• Cultivates Innovation
• Customer Focus
• Develops Talent
• Directs Work
• Drives Results
• Nimble Learning
• Optimizes Work Processes
• Self-Development