Nox Metals Purchasing Quantitative at Nox Metals responsible for improving purchasing decisions using data analysis and supplier work. Develops forecasting and cost models to optimize inventory and delivery.
Responsibilities
Success looks like. A robust demand forecasting system that reduces stockout risk and excess stock, lower inventory on hand while maintaining exceptional service, and inventory storage levels planned from geographic sales data and logistics flow.
Forecast demand by alloy, temper, thickness, form, and size using quotes, orders, customer patterns, and available market signals
Explore new products (metal types) to bring on and develop
Build data-driven cost models to inform purchasing decisions, financial forecasting, quoting, and pricing
Set and improve reorder points, safety stock, and transfer recommendations across facilities
Investigate shortages, excess stock, expedites, and recurring forecast errors
Backtest model recommendations against historical decisions to understand the effect on availability, inventory, cost, and margin
Build clear dashboards and decision tools in SQL, Python, spreadsheets, or Nox's operational systems
Check the data against physical stock and supplier commitments
Complete other duties as assigned
Strong in quantitative analysis and comfortable making decisions with incomplete operational data
Able to use SQL and Python to analyze demand and costs
Qualification
Strong quantitative analysis skillsProficient in SQL and PythonFamiliar with purchasingExperience with ERP or MRP systemsExperience with supplier negotiationsPay and benefits$90
Required
Strong quantitative analysis skills
Comfortable making decisions with incomplete operational data
Proficient in SQL and Python
Familiar with purchasing, inventory planning, operations research, industrial engineering, or applied data science in physical-goods business
Preferred
Experience in metals distribution or manufacturing
Familiarity with aerospace or defense material requirements
Experience with ERP or MRP systems
Experience with forecasting or constrained optimization