Senior Decision Scientist – Fulfillment Analytics

Best Buy•Richfield, MN

5d•Hybrid

About The Position

As the Senior Decision Scientist – Fulfillment Analytics, you will lead the design of analytical solutions that solve meaningful fulfillment problems and ensure our models translate into real-world impact. You’ll scope ambiguous business questions, validate technical approaches, and partner with data scientists and software engineers to deliver integrated solutions. Successful candidates bring clear problem framing and scoping skills, practical technical awareness, and the ability to guide cross-functional teams toward clear, data-informed decisions. This role is ideal for someone who enjoys turning complex operational challenges into durable analytical solutions. This role is hybrid, which means you will work some days at our corporate office in Richfield, Minnesota, and some days virtually from home or another non-Best Buy location. The specific work arrangements vary by role and team. The recruiter or hiring manager will provide more details during the hiring process.

Requirements

Bachelor’s degree in a quantitative field or equivalent experience.
4 or more years of experience leveraging data to solve business problems and communicate insights through executive-ready presentations and dashboards.
2 or more years of experience applying SQL, Python, R or a similar tool and using visualization tools (e.g., Power BI, Looker, Tableau).
Demonstrated ability to lead cross-functional conversations and frame analytical solutions.
2 or more years of experience applying foundational statistics (e.g., regression, hypothesis testing) to business questions.

Nice To Haves

Experience in supply chain, fulfillment, logistics, or related business domains.
Familiarity with statistical modeling, forecasting, optimization concepts, or econometric techniques used in analytical problem solving.
Experience organizing analytical roadmaps or requirements for technical teams.
Experience guiding and mentoring other decision and data scientists.
Working knowledge of modern data platform concepts (e.g., data lakes, curated base assets, data models, and semantic layers) and how to select the right asset for analytical use.

Responsibilities

Lead scoping conversations to define clear analytical problems and solution approaches in the order fulfillment domain.
Validate assumptions, interpret model outputs, and ensure analytical solutions are practical and durable.
Use a programming language to prototype, explore data, and collaborate effectively with technical partners.
Partner with product, software engineering, and business teams to align solutions to operational priorities.
Partner with data platform teams to identify the right data assets (base, modeled, or semantic) that support our ongoing data modernization and cloud-first analytical work.
Provide leadership and mentorship across the team to elevate analytical rigor and decision quality