Senior Data Scientist, Agentic AI & MLOps

The Home Depot

Atlanta (GA)

On-site

USD 140,000 - 190,000

Full time

6 days ago

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Job summary

The Home Depot in Atlanta, GA is seeking a Sr. Data Scientist to lead the development of agentic AI and production-grade ML platforms.

You will establish MLOps, LLMops, and AIOps foundations and build scalable data-driven solutions that reason over enterprise data to support merchandising decisions. Embedded with the data science team, you will partner with software engineers and business stakeholders to automate lifecycles, embed governance, and deploy tools and retrieval systems for enterprise

Qualifications

6+ years of experience in data science, machine learning, 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 utilizing 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), cloud deployment (GCP preferred), and technical collaboration with Engineering, DevOps, and SRE partners.
Demonstrated expertise in foundational MLOps/LLMOps/AIOps practices, including 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 practices (e.g., 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.

Responsibilities

35% Solution Development - Proficiently design and develop algorithms and models to use against large datasets to create business insights; Execute tasks with high levels of efficiency and quality; Make appropriate selection, utilization and interpretation of advanced analytical methodologies; Effectively communicate insights and recommendations to both technical and non-technical leaders and business customers/partners; 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
30% Project Management & Team Support - Work with project teams and business partners to determine project goals; Provide direction on prioritization of work and ensure quality of work; Provide mentoring and coaching to more junior roles to support their technical competencies; Collaborate with managers and team in the distribution of workload and resources; Support recruiting and hiring efforts for the team
20% Business Collaboration - Leverage extensive business knowledge into solution approach; Effectively develop trust and collaboration with internal customers and cross-functional teams; Provide general education on advanced analytics to technical and non-technical business partners; Deep understanding of IT needs for the team to be successful in tackling business problems; Actively seek out new business opportunities to leverage data science as a competitive advantage
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; Define best practices and develop clear vision for data analysis and model productionalization; Contribute to library of reusable algorithms for future use, ensuring developed codes are documented