Staff AI Scientist: Inventory Optimization & Forecasting

Afresh; United States; Hybrid; USD 191,760 - 287,640; Full time

Benefits offered by this job

Comprehensive medical, dental, and vision coverage

401(k) with company match

Professional development budget

Flexible paid time off

Monthly wellness stipends

Job summary

Afresh is seeking a Staff Applied Scientist to spearhead R&D efforts focused on optimizing perishable inventory control through advanced AI/ML methods. Your role involves solving complex supply chain problems, ensuring efficiency in fresh food ordering.

With a strong background in machine learning and operations research, you'll create scalable solutions that directly impact food waste reduction. The ideal candidate holds a relevant degree and has extensive industry experience, ready to make significant contributions in a collaborative environment.

Qualifications

• 8+ years of industry experience for MS candidates or 4+ years for PhD candidates.
• Experience with decision-making under uncertainty.
• Ability to communicate complex ideas effectively.

Responsibilities

• Set technical direction for core replenishment R&D.
• Model complex inventory issues and implement solutions.
• Lead R&D for new product challenges.

Skills

Machine learning

Forecasting

Operations research

Stochastic optimization

Python

Education

MS or PhD in Operations Research, Industrial Engineering, or Computer Science

Tools

Python data stack (numpy/torch/pandas)

Job description

Afresh is seeking a Staff Applied Scientist to spearhead R&D efforts focused on optimizing perishable inventory control through advanced AI/ML methods. Your role involves solving complex supply chain problems, ensuring efficiency in fresh food ordering.

With a strong background in machine learning and operations research, you'll create scalable solutions that directly impact food waste reduction. The ideal candidate holds a relevant degree and has extensive industry experience, ready to make significant contributions in a collaborative environment.