Data Scientist, Pricing (Optimization & Deep Learning)

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

The Home Depot is able to offer virtual employment of this position in the following states:

AK, AZ, CA, GA, HI, ID, IL, KS, KY, ME, MA, MI, MN, MT, NE, NV, NH, NJ, NM, NY, ND, OH, OR, RI, SD, TX, UT, VT, WA, WV, WI, WY

Position Purpose:

The Data Scientist works with price optimization based on future forecasted outcomes of price and promotions, and needs experience with optimization and leveraging Deep Learning solutions.The successful candidate will be 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. Sr. Data Scientists are expected to seek out business opportunities to leverage data science as a competitive advantage. Based on the specific data science team, this role would need to be proficient in optimization and deep learning techniques.

As a Data Scientist, you will apply advanced analytics methods and algorithms for identifying trends and providing business solutions. This role is expected to present insights and recommendations to non-technical audiences and explain the benefits and impacts of the recommended solutions. In addition, Data Scientists collaborate with business partners and cross-functional teams, requiring effective communication skills, building relationships, and focus on understanding the overall business area being supported.

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