Staff Applied Scientist (Distribution Center)

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

About the Role

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).

Why You’ll Love Working at Afresh

At Afresh, our mission to eliminate food waste starts with investing in our people. We provide a comprehensive support system designed to help you do your best work while maintaining a healthy, balanced life.

• Comprehensive Health & Wellness: Comprehensive medical, dental, and vision coverage for you and your family, with the majority of premiums covered by Afresh. We also provide dedicated mental health support and counseling services.
• Invested in Your Future: Competitive base salary, meaningful equity (U.S. employees), and a 401(k) program with a generous company match.
• Flexible & Modern Workspace: Whether you work from home or a local office, we support your setup with a home office stipend and "Coworking Wallets" for flexible workspace access.
• Growth-Obsessed Culture: We believe in continuous learning. Every employee receives an annual professional development budget to master new skills and grow their career at Afresh.
• Holistic Monthly Stipends: Beyond your paycheck, we provide monthly stipends for "Betterment" (wellness/lifestyle) and telecommunications to ensure you have what you need to thrive.
• Time to Recharge: Flexible paid time off to take the time you need to recharge.

*Full-time U.S. employees are eligible for these benefits

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: AL, AR, CA, CO, FL, GA, IL, KY, MA, MI, MT, MO, NV, NJ, NY, NC, OR, PA, TX, WA, UT, VA, WI.