- Location
- Ottawa, ON, Canada · Onsite
- Compensation
- Not stated in this snapshot
- Employment
- Full-time
- Experience level
- MID (2–5 years exp)
- Specialization
- Machine Learning, Machine Learning Engineer, Discover Technata
- Source
- Discover Technata
Support and improve a production scheduling solution for a steel coating and painting operation. You will adapt an existing optimization model; you won’t be building one from scratch.
What You’ll Do
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Reverse-engineer an existing optimization model and solver built by another team, and learn its logic, constraints and data flows
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Adapt and extend the solution for North American plant scheduling and simpler paint/coating lines
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Sequence production jobs by grouping paint colors and steel dimensions, cutting changeovers (around 20 minutes per paint change) and line downtime
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Adjust the model as operational constraints and short-notice customer orders change
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Support integration of scheduling outputs with the Oracle ERP
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Troubleshoot production issues and deliver ongoing enhancements after rollout
What You’ll Bring
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Strong Python skills, with experience implementing, modifying and troubleshooting production-grade ML or mathematical optimization solutions
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Hands-on experience with optimization solvers and machine learning platforms
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Experience tuning, retraining or reconfiguring optimization/ML models for different operational constraints
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Ability to work independently in an existing codebase with limited documentation and subject-matter knowledge
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Nice to have: Azure Machine Learning experience; a background in manufacturing scheduling, production optimization, constraint optimization or supply-chain scheduling