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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr Manufacturing AI/Data Science Engineer - **Company:** MINIMED LLC - **Location:** Los Angeles, CA, United States - **Experience:** Expert - **Salary:** $112,000.0 - $190,000.0 - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Amazon Web Services, Business Analytics Applications, Data Analysis, Computer Vision, Microsoft Azure, Cloud Computing, Cloud Database, Information Engineering, Data Integrity, Data Visualization, R (Programming Language), Statistical Hypothesis Testing, Python (Programming Language), MATLAB, Machine Learning, Minitab, Natural Language Processing, Power BI, Azure Machine Learning, Software Deployment, Statistical Process Control (SPC), SQL Databases, Data Streaming, Tableau (Software), Technical Data Management Systems, JMP (Statistical Software), GitHub Copilot, Snowflake, Information Technology, Data Analytics, Data Management, Machine Learning Operations, GXP, Databricks - **Published:** November 5, 2026 - **Apply:** https://www.careerbuilder.com/job-details/sr-manufacturing-ai-data-science-engineer-northridge-ca--e6799814-29eb-4c94-b606-8105ede207c6 ## About the Role Manufacturing, Artificial Intelligence (AI), Data Science, Diabetes, Manufacturing, Metrics, Data Science, Cloud Computing, Artificial Intelligence (AI), Root Cause Analysis, The ideal candidate has significant experience working in a manufacturing environment and a proven history of applying analytics, statistics, data science, or AI to solve manufacturing and operational challenges. They understand how products, processes, equipment, materials, quality systems, and operational decisions interact and can translate manufacturing problems into effective, data-driven solutions., * 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 relevant statistical and manufacturing methods, including root cause analysis, statistical process control, process capability analysis, hypothesis testing, regression, and design of experiments. * Working knowledge of manufacturing systems and data, including one or more of the following: 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 to investigate complex problems, develop analytical solutions, and communicate results. * Experience translating manufacturing problems into analytical approaches and converting analytical results into practical recommendations. * Ability to determine when traditional statistical methods, machine learning, AI, or other analytical techniques are appropriate for a given manufacturing problem. * 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 solutions that produce measurable operational or business value. #BetterWorkdaysStartNow, * 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 applicable 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. ## Description Must have Manufacturing experience!!! Candidates can be either of our Northridge CA office or our St. Louis Park MN office. Must be onsite 3 days per week/2 day remote.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 our 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 we design, build, and deliver our products. The ideal candidate is a technically strong data scientist with experience in supply-chain, manufacturing, or a related industry who thrives in complex, cross-functional environments. Candidates may work from either our Northridge, CA or St. Louis Park, MN office and must be onsite three days per week, with two remote days. MiniMed is seeking a Senior Manufacturing AI/Data Science Engineer who combines deep manufacturing knowledge with strong analytics and data science capabilities. This role will apply statistics, advanced analytics, machine learning, and emerging AI technologies to solve complex challenges across manufacturing, quality, supply chain, operations, and new product introduction. The successful candidate will work closely with manufacturing and cross-functional partners to investigate process and quality issues, improve manufacturing data and operational metrics, and deliver analytical solutions that improve product quality, process performance, operational efficiency, and decision-making. The role will also help identify and implement practical AI opportunities based on clearly defined manufacturing needs, data readiness, regulatory considerations, and measurable business value., * Serve as a trusted manufacturing analytics partner to manufacturing engineering, operations, quality, supply chain, and NPI teams, developing a strong understanding of production processes, equipment, materials, data flows, and key performance drivers. * 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 the 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 appropriate 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, connecting complex results to operational actions and measurable outcomes.