Staff Applied Scientist

• 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, and implement solutions to multi-stage and multi-echelon inventory optimization problems.
• Drive fundamental changes to our 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 ensuring 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 (medical, dental, vision, mental health support), competitive base salary ($191,760 - $287,640 USD), meaningful equity, 401(k) with company match, flexible workspace with home office stipend and coworking wallets, annual professional development budget, monthly stipends for wellness and telecommunications, flexible PTO.

Afresh is an AI platform for grocery that uses proprietary AI to help grocers minimize waste and maximize sales, tackling food waste as a key climate change solution. Founded in 2017, its technology is live in over 10% of the U.S. grocery market and prevented over 200 million pounds of food waste last year.