Senior AI/Data Science Engineer – Manufacturing & Operations

MiniMed is a full-stack insulin delivery company dedicated to supporting people living with diabetes through every step of their journey. They are seeking a Senior AI/Data Science Engineer to join their Data Science team, focusing on manufacturing and operations to enhance cloud data infrastructure and deliver predictive solutions that improve product design and delivery.

Responsibilities:

• Contribute to the maturation of MiniMed's manufacturing data pipelines, identifying critical data elements required for advanced analytics and partnering with data engineering teams to drive changes through requirements, development, validation, and production deployment
• Deliver measurable improvements in operational excellence and cost efficiency by converting complex operational datasets into actionable, governed insights - rationalizing fragmented reporting, establishing authoritative source-of-truth metrics, and enabling confident decision-making across the organization
• Provide advanced analytical support for root cause investigations, bringing statistical rigor and computational depth to isolate sources of variation and accelerate resolution of critical quality and process challenges
• Identify opportunities and implement modern AI and machine learning integration across manufacturing, supply chain, operations, and NPI functions, balancing technical ambition against practical realities including data readiness, regulatory considerations, and demonstrable business value. Target new technology implementation towards problem solving
• Serve as the dedicated data science partner to operational functions, developing deep domain fluency and trusted cross-functional relationships; advance the maturity of the broader Data Science team through reusable tooling, documentation standards, and technical mentorship

Requirements:

• Bachelor's degree with 4+ years of relevant experience; or advanced degree with 2+ years of relevant experience
• Bachelor's degree in Data Science, Statistics, Computer Science, Industrial Engineering, or a related field, Master's or Ph.D. in quantitative discipline preferred
• 5 or more years of applied data science or machine learning engineering experience, with meaningful experience in a manufacturing, supply chain, or industrial operations environment
• Familiarity with manufacturing execution system (MES) and ERP systems data structures
• Proficient in Python for data science and ML development, including libraries such as pandas, scikit-learn, PyTorch, and TensorFlow
• Strong SQL skills and demonstrated experience working with cloud data platforms such as Databricks, Snowflake, Azure, and AWS
• Experience developing and deploying production machine learning models and analytical pipelines
• Exposure to computer vision, NLP, or generative AI applications in an industrial or operational context
• Experience with BI and data visualization tools such as Power BI or Tableau
• Demonstrated ability to communicate complex analytical concepts clearly to non-technical business and operations stakeholders
• Proven ability to work effectively in cross-functional, matrixed environments with multiple competing priorities and ambiguous problem definitions
• Solid foundation in statistical methods including hypothesis testing, regression analysis, statistical process control, design of experiments, and process capability analysis
• Experience working in a regulated industry such as medical devices, pharmaceuticals, or aerospace, with familiarity with FDA data integrity requirements or GxP standards
• Familiarity with MLOps platforms such as MLflow, Azure Machine Learning, or AWS SageMaker
• Experience with AI-assisted coding environments such as Windsurf, GitHub Copilot, or similar
• Experience working with IoT or IoT sensor data and time-series data pipelines
• Knowledge of Lean Manufacturing or Six Sigma methodologies; Green Belt or Black Belt certification a plus