Staff Applied Scientist (Distribution Center Solutions)

• Set technical direction for core replenishment R&D, defining the modeling roadmap across demand forecasting, inventory optimization, and decision-making policy.
• Model complex problems such as inventory decay, promotions, price elasticity, and inventory uncertainty, implementing solutions to multi-stage and multi-echelon inventory optimization problems.
• Drive fundamental changes to the core system from research through production, writing rigorously tested and scalable code.
• Lead research and development for new product and business challenges, and mentor scientists and engineers.

This role directly reduces food waste by optimizing fresh food ordering, preventing over 200 million pounds of food waste annually, and ensures fresher, healthier produce for millions of people.

• MS or PhD in Operations Research, Industrial Engineering, Computer Science, Electrical Engineering, or another quantitative field, or equivalent practical experience.
• For MS candidates, 8+ years of industry experience; for PhD candidates, 4+ years of industry experience.
• Experience researching and building systems that support large-scale decision making under uncertainty.
• Ability to independently deliver high quality software implementations in the Python data stack (numpy/torch/pandas/etc).

Comprehensive health & wellness coverage, competitive base salary with meaningful equity (U.S. employees), 401(k) with company match, flexible remote work with home office stipend and coworking wallets, annual professional development budget, monthly stipends for wellness and telecommunications, and flexible PTO.

Salary range in Canada: $169,000 - $252,000 CAD (dependent on experience).

Afresh Technologies uses AI to tackle food waste in the grocery industry, empowering partners like Albertsons, Meijer, and Wakefern to drive smarter decisions across their enterprise. Founded in 2017, their platform orchestrates billions of decisions and prevented over 200 million pounds of food waste last year.