The primary purpose of the Lead Data Scientist is to lead the development of data science, machine learning (ML), and deep learning (DL) solutions to solve retail problems at scale.
Requirements
- 6 years of experience executing and deploying data science, ML, DL, and generative AI solutions, preferably in a large-scale enterprise setting (fewer years may be accepted with a master’s or doctorate degree)
- 6 years of SQL and programming experience (fewer years may be accepted with a master's or doctorate degree)
- 4 years of experience using multiple data systems and sources
Responsibilities
- Leads the full lifecycle of data science projects from inception to completion with full adherence to enterprise product and project management practices.
- Applies advanced data science, ML, and DL algorithms necessary to derive insights and develop solutions on enterprise scale providing a competitive advantage to the business.
- Prepares final recommendations through effective storytelling and actionable insights for the relevant stakeholders, including senior and executive leaders, ensuring solutions are implementable and scalable in the business.
- Maintains high-level knowledge on industry trends, emerging technologies, and their potential application in the home improvement domain.
- Leads, mentors, and inspires a team of data scientists, fostering a culture of continuous learning, collaboration, and innovation.
- Provides guidance to more junior level scientists to translate requirements into an analytical approach and plan resources accordingly.
- Collaborates and communicates with cross functional stakeholders to provide updates and manage scope of data science-oriented solutions and projects.
Other
- Bachelor's degree in mathematics, statistics, physics, economics, engineering, computer science, data or information science, or related quantitative analytic field (or equivalent work experience in lieu of degree)
- 6 years of experience working with cross-functional partners
- Doctorate Degree in mathematics, statistics, physics, economics, engineering, computer science, data or information science, or related quantitative analytic field
