Create optimization models for business problems and solutions. Define data requirements and gather and validate information, applying judgment and statistical tests. Define and design algorithms and perform proof-of-concept studies and ensure accurate and efficient implementation of algorithms. Perform algorithmic performance analysis. Interact with the client to understand business problems. Interact with end users to explain model behavior, model outputs, and calibration parameters. Study and analyze information about alternative courses of action to determine which plan will offer the best outcomes. Perform R&D of new upcoming techniques. Analyze data and generate insights using data science techniques such as regression, clustering, boosting, decision trees, etc. Specify manipulative or computational methods to be applied to models. Design, conduct, and evaluate experimental operational models in cases where models cannot be developed from existing data. Develop machine learning models for prediction problems. Develop time series models for forecasting problems. Leverage simulation modeling for data-driven design experiments. Perform validation and testing of models to ensure adequacy and reformulate models as necessary.
May telecommute from any location within the U.S.
One (1) year of experience in the job offered or a related position. Experience must include demonstrable knowledge of: Python; Git; GitLab; SQL; ML Modeling; Azure ML; Databricks; Probability & Statistics; DS & Algorithms; Heuristics & Optimization Modeling; Simulation Modeling; FlexSim, and; Spotfire.
Master’s degree (or foreign equivalent) in Supply Chain Engineering, or a related field
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