Azure Senior Data Scientist

9+ years of hands-on experience delivering data science and ML/AI solutions on cloud platforms, preferably Microsoft Azure and Databricks. BS/MS in a quantitative field such as Statistics, Mathematics, Computer Science, Engineering, AI, or Analytics. PhD strongly preferred. Proven ownership of the full MLOps lifecycle—experiment tracking, model registration, deployment, monitoring, and retraining—using Azure ML and Databricks/MLflow, ideally with CI/CD in Azure DevOps or GitHub. Ability to translate manufacturing, supply chain, or operations business problems into technical requirements. Strong consulting-grade communication skills, including storytelling with analytics and visualization. Expert-level proficiency in Python and SQL, with T-SQL preferred. Deep, hands-on experience with core data science libraries, including scikit-learn for classical and statistical ML and PyTorch for deep learning. Proven experience working in Databricks with PySpark. Hands-on use of optimization approaches commonly applied in supply chain and manufacturing. Practical knowledge of Generative AI on Azure, including prompt engineering and RAG. Hands-on experience implementing RAG and vectorization on Microsoft-first tooling. Hands-on experience building and orchestrating AI agents in the Microsoft ecosystem. Extensive experience integrating with Azure-centric data platforms. Ability to stand up and manage data and experimentation environments. Strong critical thinking, active listening, and situational awareness. Attention to detail and a bias for end-to-end ownership of deliverables. Experience delivering projects on large, complex datasets, preferably in manufacturing, supply chain, or logistics industries. Demonstrated ability to communicate effectively with technical teams, business stakeholders, and client leadership.

Lead end-to-end data science and ML engagements on Microsoft Azure, Microsoft Fabric, and Databricks. Design and implement advanced analytics solutions using Azure Machine Learning, Azure OpenAI, Databricks, and Power BI. Build modern retrieval and RAG patterns on Azure. Develop, fine-tune, and deploy models, from classical to deep learning and Generative AI. Perform hands-on LLM adaptation and package or customize models for production hosting. Translate client business requirements into technical architectures, feature roadmaps, and implementation plans. Analyze large, complex datasets to generate actionable insights. Present model results and data stories to non-technical leaders. Partner with data engineering and architecture teams to improve data capture, quality, and automation. Create reusable notebooks, components, and delivery patterns. Ensure MLOps best practices for the full model lifecycle. Manage and prioritize multiple client projects simultaneously. Contribute to Statements of Work and proposals. Stay current on Microsoft and Databricks product roadmaps. Meet with clients to understand problems and shape analytics and AI solutions. Be available for approximately 10 percent travel on an as-needed basis.