Sr Data Scientist - AI Engineer

Build production-ready AI at scale from Home Depot (THD) in Atlanta, GA (onsite). This senior role focuses on setting up MLOps, LLMOps, and AIOps operational frameworks and delivering agentic AI solutions that can reason over enterprise data and orchestrate data science models into reliable production workflows. You will work closely with both technical teams and business partners, turning advanced analytics into decisions leaders can act on.

Responsibilities

• Design and develop algorithms and models for large datasets to generate actionable business insights.
• Deliver high-quality work with strong execution efficiency across development tasks.
• Select and apply appropriate advanced analytical methodologies, including interpretation of results to drive recommendations.
• Communicate insights clearly to technical and non-technical leaders, as well as business customers and partners.
• Share project progress through reports, updates, and presentations.
• Explain impact of recommendations to support alignment and appropriate implementation.
• Partner on goals with project teams and business partners to determine project objectives.
• Guide prioritization and help ensure the quality of deliverables.
• Mentor junior roles through mentoring and coaching to strengthen technical competencies.
• Support planning and resources by collaborating with managers and the team on workload and resource distribution.
• Contribute to hiring efforts for the team.
• Bring business knowledge into the solution approach and build trust across internal customers and cross-functional teams.
• Educate stakeholders on advanced analytics for both technical and non-technical partners.
• Apply IT understanding to ensure the team can effectively tackle business problems.
• Identify opportunities to leverage data science as a competitive advantage.
• Advance technical capability by tracking key developments in data science, skills, and new data sources.
• Improve reuse by creating replicable solutions such as codified data products, project documentation, and process flowcharts so future projects can build on prior work.
• Define best practices and articulate a clear vision for data analysis and model operationalization.
• Build reusable assets by contributing to a library of documented, reusable algorithms.

Requirements

• Must be eighteen years of age or older.
• Must be legally permitted to work in the United States.
• 5+ years of work experience; minimum years of work experience listed as 5.
• 6+ years of experience in data science, machine learning engineering, AI engineering, software engineering, or MLOps with a focus on production-ready AI solutions.
• 2+ years 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 including Python, SQL, automated testing, containerization (Docker/Kubernetes), and cloud deployment (GCP preferred), with collaboration across Engineering, DevOps, and SRE partners.
• Demonstrated 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.
• Demonstrated expertise in AI observability, operational reliability, and governance: tracing, telemetry, automated alerting, anomaly detection, incident triage, eval harnesses, safety guardrails, model explainability, approval paths, fallback mechanisms, tool-use auditing, cost/latency monitoring, and human-in-the-loop controls.
• Hands-on experience with agent orchestration frameworks, structured agent communication protocols (e.g., MCP, A2A), and Infrastructure-as-Code (IaC).
• Ability to prototype lightweight tools or interfaces, evaluate technical feasibility, and document reusable architectural patterns for future production use.
• Continuous learning agility to evaluate emerging AI architectures, protocols, and operating models.
• Domain experience in merchandising, retail, ecommerce, supply chain, assortment planning, or space planning.
• Education: bachelor's degree program or equivalent degree in a field of study related to the job.

Working Conditions

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

Travel Requirements

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

Technologies

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