Staff Applied Scientist (Distribution Center Solutions)
New
A
AfreshArtificial Intelligence
Remote - Ontario, Canada; 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, WIFull-TimeStaff
Salary169,000 - 252,000 CAD per year
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Job Details
- Experience
- For candidates with an MS, 8+ years; for candidates with a PhD, 4+ years.
- Required Skills
- PythonMachine LearningNumpyPyTorchPandas
Requirements
- MS or PhD in Operations Research, Industrial Engineering, Computer Science, Electrical Engineering, or equivalent quantitative field.
- 8+ years of industry experience with an MS, or 4+ years of industry experience with a PhD.
- Experience researching and building systems that support large-scale decision making under uncertainty.
- Experience in inventory optimization, supply chain management, network optimization, forecasting, game theory, or stochastic optimization.
- Strong ability to translate business requirements into constrained optimization problems.
- Proficiency in delivering high-quality software implementations using the Python data stack (numpy, torch, pandas).
- Excellent communication and presentation skills, with the ability to explain complex mathematical concepts to non-technical stakeholders.
Responsibilities
- Set technical direction for core replenishment R&D including modeling roadmaps for demand forecasting, inventory optimization, and decision-making policy.
- Model complex supply chain challenges such as inventory decay, promotions, price elasticity, and inventory uncertainty.
- Implement solutions to multi-stage and multi-echelon inventory optimization problems.
- Drive fundamental changes to core systems from research through production, writing rigorously tested and scalable code.
- Lead research and development for new product and business challenges.
- Raise the technical bar across the Intelligence team by mentoring scientists and engineers, setting standards for experimental rigor, and reviewing designs.
- Push the boundaries of AI capabilities in both products and scientist workflows.
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