Data Scientist II

As a Data Scientist II within the AI Engineering team, you will: Problem Solving & Stakeholder Collaboration Work with business stakeholders, Solution Managers, TPMs, and engineering teams to understand business context and translate defined problems into analytical and technical approaches. Contribute to scoping discussions, surfacing trade-offs and helping refine the path from business need to AI solution. Build and deploy production AI/ML systems (ML/DL, GenAI, optimization) using Azure AI, open-source frameworks, and custom models, with ownership of model development from prototype through deployment. Apply engineering best practices for code quality, MLOps, testing, and monitoring; contribute to improving team-wide standards. Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) or consulting experience. OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 2+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results). OR equivalent experience. Hands-on experience with Azure-native platforms: Fabric, Kusto, Databricks, Synapse, Azure ML Studio. Exposure to supply chain, manufacturing, or hardware operations contexts (demand planning, yield optimization, logistics) is a plus. Experience contributing to MLOps infrastructure: CI/CD for models, automated retraining, drift detection. Familiarity with optimization methods (linear/mixed-integer programming, simulation) applied to operational problems. Systems thinking - ability to reason about trade-offs between model performance, engineering effort, and operational simplicity.