Azure Senior Data Scientist

Through passion and deep industry expertise, MCA Connect helps manufacturers succeed by unlocking innovation with actionable business insights. Our strategic solutions, innovation, and industry intelligence help manufacturers gain visibility, improve profitability, and achieve a competitive edge. Established in 2002, MCA Connect has grown into one of the largest US-based solution partners in Microsoft Business Applications and Azure Data & AI / Digital & App Innovation. Our Microsoft Specialties include Finance and Supply Chain, Analytics on Azure, Data Warehouse Migration, and Power Platform. We’re also a fifteen-time Microsoft Partner of the Year and three-time Inc. Best Workplaces award winner.

Title  Senior Data Scientist Location Fully Remote with travel up to 10% DescriptionThe Senior Data Scientist is responsible for being a partner to team members, a mentor, and directly enabling growth of the Data & AI Services team at MCA. The Senior Data Scientist is expected to lead Data Science implementations, drive innovation, upskill and mentor junior teammates, and partner with leadership to drive continuous improvement
Within a project framework, the Senior Data Scientist is responsible for building client relationships, effectively utilizing Agile project delivery methodologies, implementing scalable and efficient technical architectures to satisfy client requirements, and performing hands on implementations of Microsoft data solutions for MCA Connect’s current and future customers/projects

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.
• 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 Azu