Sr. Data Scientist - Network Modeling & Optimization

Sr. Data Scientist - Network Modeling & Optimization

The Sr. Data Scientist – Network Modeling & Optimization will be a senior technical contributor responsible for leading complex analytical and modeling projects and supported by a small dedicated team of Data Scientists. This role’s primary responsibility will be updating, running and improving existing Supply Chain network models to support business planning and one-off decisions both tactical and strategic. The role will be hands‑on.

The role will also assist and advise in the development of new models and readiness for production.

The Network Modeling & Optimization team within Supply Chain Analytics develops analytical capabilities that help Best Buy evaluate its supply chain network across cost, capacity, customer service, product flow, inventory placement, and operational feasibility.

This role is hybrid, which means you will be required to work up to three days/week on site at the Best Buy corporate office in Richfield, Minnesota. You can work virtually from home or another non Best Buy location on days not expected in the office. The recruiter or hiring manager will provide more details during the hiring process.

• Develop and maintain analytical inputs and optimization models representing facilities, transportation lanes, demand, product flows, costs, capacities, service requirements, and business rules.
• Evaluate strategic and tactical scenarios involving facility location, network assignments, capacity, sourcing, inventory placement, transportation, and product flow.
• Validate model behavior, diagnose infeasibilities and data-quality issues, and clearly communicate assumptions, limitations, tradeoffs, and recommendations to business and technical stakeholders
• Translate ambiguous supply chain questions into structured analytical problems by defining objectives, available decisions, constraints, assumptions, data requirements, and expected outputs.
• Ability to translate complex physical processes into simplified functions that preserve the essential real-world characteristics but simple enough to become inputs for mathematical models.
• Support complex network modeling and optimization projects from business‑problem definition through model development, validation, interpretation, and recommendation.
• Provide technical guidance and mentorship to Data Scientists and analysts while establishing reusable modeling practices, documentation standards, and quality controls.

• Bachelor’s degree in Operations Research, Industrial Engineering, Mathematics, Statistics, Computer Science, Economics, Engineering, Supply Chain Analytics, or a related quantitative field—or equivalent experience.
• 4+ years of relevant experience in data science, operations research, optimization, supply chain analytics, forecasting, simulation, or a related analytical field.
• 4+ years of leading complex analytical projects and translate ambiguous business questions into structured modeling approaches.
• 4+years hands‑on experience using data analytics (e.g., SQL, Python, R), and machinelearningand/or optimization tools (i.e., Python, R,Gurobi, Vertex AI).
• Strong knowledge of optimization, operations research, statistical modeling, forecasting, simulation, machine learning, or another advanced analytical discipline.
• Ability to explain technical concepts, model results, and business recommendations clearly to technical and non‑technical audiences.

• Advanced degree in Operations Research, Industrial Engineering, Mathematics, Statistics, Computer Science, Supply Chain Analytics, or a related quantitative field.
• Experience with supply chain network design, transportation, distribution, fulfillment, inventory, logistics, or capacity planning.
• Experience developing optimization models using methods such as linear programming, mixed‑integer programming, network flow, facility location, or assignment modeling.
• Experience defining model inputs, decision variables, constraints, objective functions, scenarios, and outputs—and diagnosing model infeasibilities.
• Familiarity with AIMMS or comparable optimization platforms, libraries, or solvers. Direct…