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Mid-level ML/data role in metro with generalist forecasting skills and known vendor brand increases competition.
Requires supply-chain forecasting expertise, but core ML/analytics skills remain transferable across industries.
Explicit 2-5 year requirement plus domain and tool mandates moderately tighten shortlisting.
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Analyze large and complex supply chain data sets for demand forecasting and actionable insights.
Develop, tune, and implement machine learning and optimization models to improve supply chain efficiency and reduce costs.
Act as a subject matter expert to advise stakeholders on supply chain analytics tools, strategies, and continuous process improvements.
Bachelor’s or Master’s degree in Data Science, Computer Science, Industrial Engineering, Operations Research, Supply Chain Management, or related field.
2-5 years of experience in data science or supply chain operations analytics involving forecasting projects and statistical/ML modeling.
Proficiency in Python, R, SQL, applied statistics, and experience with supply chain management platforms/processes.
Hands-on experience with machine learning techniques (regression, classification, clustering) and optimization models.
Experienced in applying statistical and machine learning methods specifically for supply chain forecasting and optimization.
Able to translate complex analytical insights into clear recommendations and communicate effectively with non-technical stakeholders.
Comfortable working independently and collaboratively in a consulting environment focused on data-driven supply chain process improvements.