Sr Manufacturing AI/Data Science Engineer
About The Position
MiniMed is seeking a Senior Manufacturing AI/Data Science Engineer to join its Data Science team in a dedicated capacity supporting manufacturing, supply chain, operations, and new product introduction (NPI). The position may be based at the Northridge, California, or St. Louis Park, Minnesota, office and requires onsite work three days per week, with two days remote. The role focuses on advancing cloud data infrastructure for manufacturing data sources, establishing trusted operational metrics, supporting root-cause investigations with advanced analytics, and delivering predictive and AI-driven solutions to improve product design, manufacturing, and delivery. The successful candidate will bring strong data science expertise and experience in manufacturing, supply chain, or a related industry, and will work across complex, cross-functional environments. The position serves as a dedicated data science partner to operational teams. It also supports the broader Data Science team through reusable tools, documentation standards, and technical mentorship. Occasional visits to manufacturing-floor environments may be required; the work is primarily in an office and hybrid environment.
Requirements
- Bachelor's degree in Data Science, Statistics, Computer Science, Industrial Engineering, or a related quantitative field.
- At least 5 years of applied data science or machine-learning engineering experience, including meaningful experience in manufacturing, supply chain, or industrial operations.
- Familiarity with manufacturing execution system (MES) and ERP data structures.
- Proficiency in Python for data science and machine-learning development, including libraries such as pandas, scikit-learn, PyTorch, and TensorFlow.
- Strong SQL skills and demonstrated experience 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.
- Ability to clearly communicate complex analytical concepts to non-technical business and operations stakeholders.
- Ability to work effectively in cross-functional, matrixed environments with competing priorities and ambiguous problem definitions.
Nice To Haves
- Master's degree preferred; a Master's or Ph.D. in a quantitative discipline is preferred.
- Strong foundation in statistical methods, including hypothesis testing, regression analysis, statistical process control, design of experiments, and process capability analysis.
- Experience 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 tools.
- Experience 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 is a plus.
Responsibilities
- Contribute to the development and maturation of manufacturing data pipelines by identifying data elements needed for advanced analytics and partnering with data engineering teams on requirements, development, validation, and production deployment.
- Turn complex operational datasets into governed, actionable insights that improve operational excellence and cost efficiency, including rationalizing fragmented reporting and establishing authoritative source-of-truth metrics.
- Provide advanced analytical support for root-cause investigations, using statistical and computational methods to identify sources of variation and help resolve quality and process challenges.
- Identify and implement AI and machine-learning applications across manufacturing, supply chain, operations, and NPI, taking account of data readiness, regulatory considerations, and demonstrable business value.
- Serve as a data science partner to operational functions by developing domain knowledge and cross-functional relationships.
- Support the broader Data Science team through reusable tooling, documentation standards, and technical mentorship.
Benefits
- Competitive salary and flexible benefits package.
- Short-term incentive (STI) eligibility.
- Health insurance, dental insurance, and vision insurance for eligible regular employees.
- Health Savings Account and Healthcare Flexible Spending Account access.
- Life insurance and long-term disability leave.
- Dependent daycare spending account.
- 401(k) plan with company match.
- Short-term disability coverage.
- Paid time off and holidays.
- Employee Stock Purchase Plan participation.
- Employee Assistance Program access.
- Eligible employees may have access to a Non-qualified Retirement Plan Supplement and Capital Accumulation Plan, subject to IRS minimum earnings requirements.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
