Staff Applied Scientist

The Afresh Intelligence team is responsible for the development and performance of AI/ML models that power our core replenishment technology. Our models are directly responsible for ordering millions of dollars of fresh inventory across the world every day. Fresh food ordering is an extremely complex high-dimensional decision-making problem, and we face the complex challenges presented by decaying product, uncertain shelf lives, varying consumer demand, stochastic arrival times, extreme weather events, and tight performance constraints (to name a few). We tackle these problems with a mix of machine learning, large-scale simulation, and optimization technologies.

We are looking for a Staff Applied Scientist to lead R&D work at Afresh. You will take your existing knowledge of machine learning, forecasting, operations research, and stochastic optimization and apply it to the challenging and important problem of perishable inventory control. You will research, implement, and rigorously validate improvements to our core replenishment system. This will include modeling consumer demand, item-level perishability, and complex multi-echelon supply chains. Your work will be visible from day one, will make a substantial impact on decreasing food waste, and will lead to fresher, healthier produce for millions of people across the world.

What You’ll Do

• Set technical direction for core replenishment R&D — define the modeling roadmap across demand forecasting, inventory optimization, and decision-making policy, and align it with product and business strategy.
• 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 — we are not an analytics team.
• Lead research and development for new product and business challenges.
• Raise the technical bar across the Intelligence team: mentor scientists and engineers, set standards for experimental rigor, and review designs and results.
• Push the boundaries of AI capabilities in both products and scientist workflows.

What Makes You a Great Fit

• MS or PhD in Operations Research, Industrial Engineering, Computer Science, Electrical Engineering, or another quantitative field, or equivalent practical experience.
• For candidates with an MS, 8+ years of industry experience; for candidates with a PhD, 4+ years of industry experience.
• Experience researching and building systems that support large-scale decision making under uncertainty.
• Prior experience in areas such as inventory optimization, supply chain management, network optimization, forecasting, game theory, decision analysis, stochastic optimization, approximate dynamic programming, or related fields is a plus.
• Excellent communication and presentation skills. You should be able to explain complex mathematical ideas to product teams in plain English and easily translate business requirements into constrained optimization problems.
• Ability to independently deliver high quality software implementations of your solutions in the Python data stack (numpy/torch/pandas/etc). Prior experience with Python is not required.
• Nice to Have skills: understanding of ML Platform and a passion for mentorship

This position is not eligible for company sponsorship.

Salary Range in U.S.: $191,760 - $287,640 (dependent on experience).