Afresh is on a mission to eliminate food waste and make fresh food accessible to all. Our first A.I.-powered solution optimizes ordering, forecasting, and store operations for fresh food departments in brick-and-mortar grocers. With our Fresh Operating System, regional and national grocery retailers have placed $1.6 billion in produce orders across the US and we've helped our partners prevent 34 million pounds of food from going to waste. Working at Afresh represents a one-of-a-kind opportunity to have massive social impact at scale by leveraging uncommonly impactful software – we hope you'll join us!

About the Role

The Prediction, Optimization, and Planning (POP) team builds Afresh's 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. 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.

As an Applied Scientist at Afresh, you will take your existing knowledge of forecasting, simulation, 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.

• You will work on improving the core models of our system: demand forecasting, inventory optimization, and simulation. You will also lead research development for new product and business challenges. You will model the complex problems of inventory decay, promotions, price elasticity, and inventory uncertainty, and implement solutions to multi-stage and multi-echelon inventory optimization problems.
• In your first 3 months, you will acquire an encyclopedic knowledge of perishable inventory control and Afresh's core decision making problem. You will gain proficiency in our data manipulation, transformation, and simulation tools, and you'll test an experimental improvement to our demand forecasting, ordering, or simulation models.
• By the end of your first 6 months, you will have proposed, implemented, and rigorously validated an improvement to our core modeling system.
• By the end of your first year, you will have led the implementation of fundamental changes to our core system and led research into new product areas (warehouse level replenishment, production planning).
• We need to make optimal ordering decisions for millions of items for weeks at a time, and our system must be fault-tolerant to an extreme. Our partners rely on our system to order millions of dollars of inventory every week, and so your code must be rigorously validated, tested, and bug-proof.

Skills and Experience

The following represents attributes our ideal candidate possesses. We encourage all highly qualified candidates to apply, even if they do not fulfill all the listed criteria

• 5+ years of industrial or academic experience building systems that deal with large-scale decision making under uncertainty. Some possible prior research areas are inventory optimization, supply chain management, network optimization, forecasting, game theory, decision analysis, or stochastic and approximate dynamic programming.
• 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).

Afresh is committed to pay equity and providing highly competitive cash compensation, equity, and benefits package. Afresh conducts a pay equity audit twice each year to ensure that jobs of similar scope and impact are paid similar amounts. The final compensation offered for this role will be based on multiple factors such as the role’s scope, complexity, internal equity, the candidate’s experience/expertise, and success through the interview process.

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About Afresh

Founded in 2017, Afresh is working on the #1 solution to curb climate change: reducing food waste. By combining human insight and transformative technology, we're helping grocers provide fresher food to customers at more affordable prices.

Afresh sits at an incredible intersection of positive social impact, rocket ship financial growth, and cutting-edge technology. Our best-in-class AI research has been published in top journals including ICML, and we've raised over $148 million in funding from investors including former co-CEO of Whole Foods Market Walter Robb and Eric Schmidt's Innovation Endeavors.

Fresh is the past, present, and future of our food system – the waste we create today will impact our planet for years to come. Join us as we continue to build a vibrant, diverse, and inclusive team that embodies our company’s values of proactivity, kindness, candor, and humility.

Afresh provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity/expression, marital status, pregnancy or related condition, or any other basis protected by law.

Here at Afresh, many of our employees work remotely provided that they reside in one of the following states: AR, CA, CO, FL, GA, IL, KY, MA, MI, MT, MO, NV, NJ, NY, NC, OR, PA, TX, WA, WI. However, there may be key roles that will require a candidate/employee to be local to our San Francisco, CA office. In which case this requirement will be included in the job posting details under "Skills and experience" for reference.