Data ScientistWith a career at The Home Depot, you can be yourself and also be part of something bigger.

Position Purpose: Supply Chain Economics owns the logic and data products that bridge supply chain operations with finance. This suite of datasets, analytics and models are key for financial processes, operational initiatives, merchant allocations and more. The team aims to continue to modernize its data products and provide insights into the costs and operational levers of The Home Depot's supply chain. The Data Scientist is responsible for supporting data science initiatives that drive business profitability, increased efficiencies and improved customer experience. This role applies industry-leading analytical methodologies for working with large datasets to extract meaningful business insight and creatively solve business problems. Data Scientists are also responsible for ensuring that developed codes are documented into a library of reusable algorithms. Based on the specific data science team, this role would need to be knowledgeable in one or more data science specializations, such as optimization, computer vision, recommendation, search or NLP.

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