Azure Senior Data Scientist

Responsibilities

• Lead end-to-end data science and ML engagements on Microsoft Azure, Microsoft Fabric, and Databricks to solve manufacturing, supply chain, and operations use cases for MCA clients.
• Design and implement advanced analytics solutions using Azure Machine Learning, Azure OpenAI, Databricks (PySpark/MLflow), and Power BI, ensuring they are secure, scalable, and aligned to MCA delivery standards.
• Build modern retrieval and RAG patterns on Azure (vectorization, similarity search) to expose knowledge from SOPs, service histories, maintenance logs, and data sources commonly associated with manufacturing, supply chain, and logistics.
• Develop, fine-tune, and deploy models across the spectrum—from classical and statistical approaches (forecasting, optimization, anomaly detection) to deep learning and Generative AI—selecting the simplest viable approach for the business problem.
• Perform hands-on LLM adaptation (such as LoRA/QLoRA) and package or customize models for production hosting in Azure and Databricks.
• Translate client business requirements into technical architectures, feature roadmaps, and implementation plans, and advise when a regression or optimization solution is more appropriate than a complex GenAI approach.
• Analyze large, complex, and mixed (structured and unstructured) datasets to generate actionable insights for plant, quality, and supply chain stakeholders.
• Present model results, data stories, and recommended actions in a way that non-technical leaders can understand and operationalize.
• Partner with data engineering and architecture teams to improve data capture, data quality, and automation across Azure data lakes, warehouses, and ERP/MES sources.
• Create reusable notebooks, components, and delivery patterns to raise the maturity of the Manufacturing Intelligence team and accelerate future projects.
• Ensure MLOps best practices for the full model lifecycle, including tracking, registry, CI/CD, monitoring, and governance using Azure ML and Databricks.
• Manage and prioritize multiple client projects simultaneously, including requirements gathering, scope definition, timeline coordination, and status reporting.
• Contribute to Statements of Work and proposals that clearly define services, effort, and the Azure/Databricks solution approach.
• Stay current on Microsoft and Databricks product roadmaps and industry changes and recommend adoption where it improves client outcomes.
• Meet with clients to understand problems, validate assumptions, and shape analytics and AI solutions.
• Be available for approximately 10 percent travel on an as-needed basis.

Qualifications

• 9+ years of hands-on experience delivering data science and ML/AI solutions on cloud platforms, preferably Microsoft Azure and Databricks; experience with data engineering patterns is a plus.
• Degree in a quantitative field such as Statistics, Mathematics, Computer Science, Engineering, AI, or Analytics, or an equivalent combination of education and experience.
• 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 by partnering with business stakeholders, data scientists, and data/solution architects.
• Strong consulting-grade communication skills, including storytelling with analytics and visualization, to explain model behavior and drive decisions with non-technical audiences.
• Expert-level proficiency in Python and SQL, with T-SQL preferred for Azure SQL/Synapse/Fabric environments.
• 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, plus hands-on use of optimization approaches commonly applied in supply chain and manufacturing—such as mixed-integer/linear programming for planning and scheduling, network/routing optimization, and constraint-based solvers—using libraries like OR-Tools or SciPy optimize.
• Practical knowledge of Generative AI on Azure, including prompt engineering, retrieval-augmented generation (RAG), and the ability to identify and prioritize high-value LLM use cases in a business context.
• Hands-on experience implementing RAG and vectorization on Microsoft-first tooling (for example Azure AI Search/Vector Search, Azure OpenAI, Databricks vector stores or Lakehouse-based retrieval pipelines), including document ingestion, chunking, and securing access to manufacturing and supply chain content.
• Hands-on experience building and orchestrating AI agents in the Microsoft ecosystem (Azure AI Studio/Agents, Azure OpenAI with tool/function calling, and integrations to Databricks/Fabric or REST backends), including securing those agents against tenant data and conditioning them for manufacturing and supply chain workflows.
• Extensive experience integrating with Azure-centric data platforms, including data lakes, data warehouses, Delta/Parquet, NoSQL stores, and APIs.
• Ability to stand up and manage data and experimentation environments to support rapid model development and evaluation.
• Strong critical thinking, active listening, and situational awareness to infer business needs, understand context, and turn loose direction into concrete analytical solutions.
• Attention to detail and a bias for end-to-end ownership of deliverables in a consulting/project setting.
• 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.