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Mid-level ML role, metro location, and broad platform requirements increase candidate competition.
Requires enterprise manufacturing and MLOps experience, so moderately industry-specific but transferable skills exist.
Explicit 5-10 years plus many mandatory cloud, Databricks, and MLOps tools makes filters strict.
Lead design, development, and deployment of advanced analytics, machine learning, and AI-driven solutions using cloud platforms like Azure, Databricks, and Microsoft Fabric.
Develop and maintain data pipelines, data models, and enterprise reporting solutions to enhance operational efficiency, product quality, and strategic decision-making.
Partner with business stakeholders and technical teams to deliver scalable analytical solutions, support enterprise data management, and ensure adoption and governance of data and AI assets.
5-10 years experience in Data Science, Analytics, Data Engineering, Business Intelligence, or Enterprise Data Management.
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Information Systems, or related field (Master's considered an asset).
Proficiency with Python, R, SQL, machine learning, statistical analysis, and cloud platforms (Microsoft Azure, Azure Data Factory, Azure Synapse, Databricks, Microsoft Fabric).
Experience working with cross-functional international teams and delivering analytics solutions in manufacturing, industrial, or global enterprise environments.
Experienced in enterprise-scale data science projects with proven ability to deliver production-ready AI/ML models and automated analytical solutions.
Strong hands-on expertise in Microsoft Azure ecosystem, Databricks Lakehouse platform, and BI tools like Power BI for enterprise reporting and dashboard development.
Able to bridge business and technical teams by translating complex data insights into actionable business recommendations and participating in digital transformation initiatives.