Sr Manufacturing AI/Data Science Engineer

MiniMed is seeking a Senior Manufacturing AI/Data Science Engineer to join our Data Science team in a dedicated capacity supporting manufacturing, supply chain, operations, and new product introduction (NPI) functions. This role is responsible for accelerating the evolution of cloud data infrastructure for manufacturing data sources, solidifying trusted operational metrics, supporting root cause investigations with advanced analytics, and progressively delivering predictive and AI-driven solutions that improve how products are designed, built, and delivered. Candidates may work from either the Northridge, CA or St. Louis Park, MN office and must be onsite three days per week, with two remote days.

Responsibilities

Serve as a trusted manufacturing analytics partner to manufacturing engineering, operations, quality, supply chain, and NPI teams; Lead and support data-driven investigations into yield loss, scrap, defects, equipment performance, downtime, process variation, throughput, capacity, cycle time, and other manufacturing and quality challenges; Apply statistical and analytical methods to identify sources of variation, evaluate potential causes, quantify relationships, and accelerate resolution of complex manufacturing problems; Partner with data engineering, IT, and manufacturing systems teams to improve the availability, context, quality, reliability, and governance of manufacturing data; Apply methods such as statistical process control, process capability analysis, design of experiments, regression, multivariate analysis, time-series analysis, predictive modeling, machine learning, computer vision, or generative AI based on the specific manufacturing problem; Identify practical opportunities to apply AI and machine learning across manufacturing, quality, supply chain, operations, and NPI, balancing technical potential with data readiness, regulatory requirements, implementation feasibility, and expected business value; Communicate analytical findings and recommendations clearly to manufacturing teams, technical specialists, cross-functional partners, and leadership

Requirements

Bachelor's degree in Manufacturing Engineering, Industrial Engineering, Mechanical Engineering, Chemical Engineering, Electrical Engineering, Quality Engineering, Operations Research, Data Science, Statistics, Computer Science, or a related technical or quantitative field; Five or more years of relevant professional experience, including significant experience in manufacturing, manufacturing engineering, process engineering, quality engineering, industrial operations, operational excellence, or a closely related environment; Demonstrated history of applying statistics, analytics, data science, machine learning, or AI to solve manufacturing, quality, supply chain, or operational challenges; Practical experience with root cause analysis, statistical process control, process capability analysis, hypothesis testing, regression, and design of experiments; Working knowledge of manufacturing systems and data, such as manufacturing execution systems, ERP systems, equipment data, sensor data, process parameters, quality systems, electronic batch or device history records, and production master data; Proficiency using Python, R, SQL, Minitab, JMP, MATLAB, or similar analytical tools; Experience translating manufacturing problems into analytical approaches and converting results into practical recommendations; Ability to determine when traditional statistical methods, machine learning, AI, or other analytical techniques are appropriate; Experience collaborating across manufacturing, engineering, quality, supply chain, IT, data engineering, and leadership functions in a cross-functional or matrixed environment; Proven ability to manage multiple priorities, work through ambiguous problem definitions, and deliver measurable operational or business value

Nice to have

Master's degree or PhD in an engineering, scientific, data science, statistics, computer science, or related quantitative discipline; Experience working in a regulated manufacturing industry such as medical devices, pharmaceuticals, aerospace, automotive, or another highly controlled production environment; Familiarity with data integrity, validation, quality-system, or regulatory requirements, including FDA or GxP expectations; Knowledge of Lean Manufacturing, Six Sigma, DMAIC, or related continuous-improvement methodologies; Green Belt or Black Belt certification is a plus; Experience with computer vision, time-series modeling, anomaly detection, predictive maintenance, natural language processing, or generative AI applications relevant to manufacturing and operations; Experience with cloud data and analytics platforms such as Databricks, Snowflake, Microsoft Azure, or AWS; Experience with data visualization and business-intelligence tools such as Power BI or Tableau; Familiarity with production deployment and MLOps practices or platforms such as MLflow, Azure Machine Learning, or AWS SageMaker; Experience working with IoT, equipment, historian, control-system, or high-frequency sensor data; Familiarity with AI-assisted development tools such as GitHub Copilot, Windsurf, or similar environments

Benefits

Competitive salary and flexible benefits package; Short-term incentive (Short Term Incentive, STI) eligibility; Health, dental, and vision insurance; Health Savings Account; Healthcare Flexible Spending Account; Life insurance; Long-term disability leave; Dependent daycare spending account; Incentive plans; 401(k) plan with company match; Short-term disability coverage; Paid time off and holidays; Employee Stock Purchase Plan; Employee Assistance Program; Non-qualified Retirement Plan Supplement and Capital Accumulation Plan (subject to IRS minimum earnings requirements)