Sr AI/Data Science Engineer

MiniMed•Northridge, CA

1d•Hybrid

About The Position

At MiniMed, you can begin a lifelong career of exploration and innovation, while helping make a difference in the lives of people living with diabetes around the globe. You'll lead with purpose, breaking down barriers to innovation for a more connected, compassionate world. Candidates can work 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 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.

Requirements

Bachelor's degree in Data Science, Statistics, Computer Science, Industrial Engineering, or a related quantitative field; Master's degree 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

Nice To Haves

Master's or Ph.D. in a quantitative discipline
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

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