Lead Data Scientist - Pro

Home Depot•Atlanta, GA

15d•Onsite

About The Position

The Lead Data Scientist is responsible for leading data science initiatives that drive business profitability, increased efficiencies and improved customer experience. This role assists in the development of the Home Depot advanced analytics infrastructure that informs decision making by applying expertise of both business and Advanced Analytics Modeling techniques. Lead Data Scientists focus on seeking out business opportunities to leverage data science as a competitive advantage. Based on the specific data science team, this role has expertise in one or more data science specializations, such as optimization, computer vision, recommendation, search or NLP. As a Lead Data Scientist, you will be responsible for large data science projects, identifying opportunities to leverage best technology and approach, and mentoring data scientists on the project team. This role is expected to own the library of reusable algorithms for future use, ensuring developed codes are documented. This role supports the building of skilled and talented data science teams by providing input to staffing needs and participating in the recruiting and hiring process. In addition, this role leads data science communities across several business units.

Requirements

Must be eighteen years of age or older.
Must be legally permitted to work in the United States.
Demonstrated expertise in Advanced analytic modeling, statistics or BI preferably in retail
Masters in a quantitative field (Computer Science, Math, Statistics, etc.) or equivalent work experience
10+ years of experience in business intelligence, advanced analytics, and/or AI/ML fields.
Experience designing, building, and maintaining scalable data pipelines to support ingestion, processing, and transformation from multiple sources (e.g., databases, APIs, streaming).
Experience in a modern scripting language (preferably Python)
Proficient in Data querying (SQL/BQ) and visualization (preferably tableau)
Proficient utilizing statistical techniques to identify key insights that help solve business problems
Knowledgeable in Prescriptive Modeling like optimization, computer vision, recommendation, search, or traditional NLP is a plus.
Familiarity or hands-on experience in designing, developing, and deploying autonomous AI agents or multi-agent systems using modern LLM orchestration frameworks.
Experience with Retrieval-Augmented Generation (RAG) architectures, prompt engineering, and enabling LLMs with tool/function calling capabilities to interact with external APIs and databases.
Understanding of evaluating, testing, and monitoring generative AI models and agents in production environments to ensure reliability, safety, and performance.
Demonstrated experience in predictive modeling, data mining, and data analysis
Experience in training or mentoring associates is a plus
Experience building agents with Google Gemini ADK or copilot is a plus
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.

Nice To Haves

Masters in a quantitative field (Computer Science, Math, Statistics, etc.) or equivalent work experience
Knowledgeable in Prescriptive Modeling like optimization, computer vision, recommendation, search, or traditional NLP is a plus.
Familiarity or hands-on experience in designing, developing, and deploying autonomous AI agents or multi-agent systems using modern LLM orchestration frameworks.
Experience with Retrieval-Augmented Generation (RAG) architectures, prompt engineering, and enabling LLMs with tool/function calling capabilities to interact with external APIs and databases.
Understanding of evaluating, testing, and monitoring generative AI models and agents in production environments to ensure reliability, safety, and performance.
Experience in training or mentoring associates is a plus
Experience building agents with Google Gemini ADK or copilot is a plus
No additional education
No additional years of experience
None

Responsibilities

Utilize expertise when designing and developing algorithms and models to use against large datasets to create business insights
Make appropriate selection, utilization and interpretation of advanced analytics methodologies
Effectively communicate insights and recommendations to both technical and non-technical leaders and business customers/partners
Clearly communicate impacts of recommendations to drive alignment and appropriate implementation
Lead and manage large and complex projects and teams
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
Serve as a technical subject matter expert (SME) for one or more data science methods, both predictive and prescriptive
Lead data science communities across several business units
Leverage extensive business knowledge into solution approach
Effectively develop trust and collaboration with internal customers and cross-functional teams
Provide technical education on advanced analytics to data science community
Partner with IT to understand potential for new tools and ways to maintain technical agility for data science
Actively seek out new business opportunities to leverage data science as a competitive advantage
Seek further knowledge on key developments within data science by attending conferences and publishing papers
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
Ownership of library of reusable algorithms for future use, ensure developed code/models are documented
Develop mastery in one or more prescriptive modeling techniques, like optimization, computer vision, recommendation, search or NLP