Staff Applied Scientist – Distribution Center Solutions

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Stellenbeschreibung:

Responsibilities

Lead R&D work at Afresh for the development and performance of AI/ML models that power replenishment technology.
Model consumer demand, item-level perishability, and complex multi-echelon supply chains.
Drive fundamental changes to the core system from research through production, writing rigorously tested and scalable code.
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.

Requirements

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.

Core Competencies

Demonstrates expertise in developing and implementing AI/ML models for replenishment technology, with a strong focus on inventory optimization and supply chain management. Capable of mentoring teams and communicating complex concepts effectively to diverse stakeholders.