Sr Applied Scientist - Allocation Optimization - Remote

Location: Irvine, CA (remote acceptable)

Duration: 9-month contract

About the role

Hands-on mathematical optimization for regional store allocation. Extend existing constraint-based, multi-objective allocation framework to new regional store configurations. Adapt models to regional business rules (store capacity, presentation minimums, lead times, clearance logic). Model retraining and tuning per region as stores come online. Support explainability outputs for regional business teams.

Responsibilities

• Develop and extend constraint-based, multi-objective allocation models for regional store configurations
• Adapt optimization models to regional business rules including store capacity, presentation minimums, lead times, and clearance logic
• Perform model retraining and tuning as new stores come online in each region
• Generate explainability outputs for regional business teams
• Integrate models with SageMaker and Lambda
• Operate within a federated data architecture (central model code, regional data)

Requirements

• Expertise in mathematical optimization: linear programming, constraint satisfaction, multi-objective techniques
• Proficiency in Python with PuLP and OR-Tools; familiarity with Gurobi is a plus
• Experience with retail inventory allocation and replenishment logic
• Comfortable working in a federated data architecture

Preferred Qualifications

• Operations research background
• Experience scaling optimization models across geographies