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Mid-level ML role in a metro at a well-known brand with broad skill requirements.
Core ML skills transferable but supply-chain and CPG domain experience increases specialization.
Mandatory 4+ years plus specific ML, Python, SQL, GCP, Airflow, and deployment requirements.
Lead end-to-end development and deployment of predictive models, simulations, and optimization solutions for global supply chain functions including Procurement, Manufacturing, NetOps, Customer Service, and Logistics.
Drive projects from scoping through execution and adoption, working closely with global, cross-functional supply chain teams and business stakeholders.
Develop and operationalize scalable ML/AI models using Python, SQL, cloud platforms (Google Cloud), and ML deployment tools like Airflow, Docker, and Kubernetes.
Bachelor's degree in Computer Science, Information Technology, Business Analytics/Data Science, Economics, Statistics, or related fields.
4+ years of experience in building statistical and machine learning models with demonstrated impact.
Proficiency in Python (mandatory), SQL, and hands-on experience with ML techniques including regression, random forest, gradient boosting, SVM, clustering, and Bayesian methods.
Experience deploying models in cloud environments (Google Cloud Platform), using Airflow, Docker, and modern development workflows (GitHub).
Experience with supply chain processes, data, and metrics, especially within the CPG (consumer packaged goods) industry.
Proven ability to collaborate directly with business stakeholders and translate analytics into actionable insights driving measurable business value.
Technical aptitude in advanced analytics including working with generative AI, agentic coding platforms, and full-stack tool prototyping for business applications.