Sr Manufacturing AI/Data Science Engineer

MiniMed•Los Angeles, CA

8d•$112,000 - $190,000•Hybrid

About The Position

MiniMed is seeking a Senior Manufacturing AI/Data Science Engineer to support manufacturing, supply chain, operations, and new product introduction (NPI). The role applies statistics, advanced analytics, machine learning, and emerging AI technologies to challenges across manufacturing, quality, supply chain, operations, and NPI. It focuses on evolving cloud data infrastructure for manufacturing data, establishing trusted operational metrics, supporting root-cause investigations, and delivering predictive and AI-driven solutions that improve product quality, process performance, operational efficiency, and decision-making. The successful candidate will partner with manufacturing and cross-functional teams to investigate process and quality issues, improve manufacturing data and operational metrics, and identify practical AI opportunities based on manufacturing needs, data readiness, regulatory considerations, implementation feasibility, and measurable business value. Candidates may work from either the Northridge, California, or St. Louis Park, Minnesota, office. The position requires working onsite three days per week and remotely two days per week. MiniMed offers a competitive salary and flexible benefits package. The stated U.S. salary range is $112,000–$190,000, and the position is eligible for a short-term incentive. The role is intended for someone with significant manufacturing experience who can translate complex operational problems into effective, data-driven solutions.

Requirements

Bachelor’s degree in a relevant engineering, quantitative, or technical field, such as manufacturing, industrial, mechanical, chemical, electrical, or quality engineering; operations research; data science; statistics; or computer science.
At least five 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 experience applying statistics, analytics, data science, machine learning, or AI to 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 MES, ERP, equipment or sensor data, process parameters, quality systems, electronic batch or device history records, or production master data.
Proficiency with Python, R, SQL, Minitab, JMP, MATLAB, or similar analytical tools.
Ability to translate manufacturing problems into analytical approaches and turn results into practical recommendations.
Ability to assess whether 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 in a cross-functional or matrixed environment.
Ability to manage multiple priorities, work through ambiguous problem definitions, and deliver solutions with measurable operational or business value.

Nice To Haves

Master’s degree or PhD in an engineering, scientific, data science, statistics, computer science, or related quantitative discipline.
Experience 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 for 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 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.

Responsibilities

Serve as a manufacturing analytics partner to manufacturing engineering, operations, quality, supply chain, and NPI teams, developing an understanding of production processes, equipment, materials, data flows, and 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 resolution of manufacturing problems.
Partner with data engineering, IT, and manufacturing systems teams to improve the availability, context, quality, reliability, and governance of manufacturing data.
Select and apply appropriate methods, including statistical process control, process capability analysis, design of experiments, regression, multivariate analysis, time-series analysis, predictive modeling, machine learning, computer vision, or generative AI.
Identify practical AI and machine-learning opportunities across manufacturing, quality, supply chain, operations, and NPI, considering data readiness, regulatory requirements, implementation feasibility, and expected business value.
Communicate analytical findings and recommendations to manufacturing teams, technical specialists, cross-functional partners, and leadership, connecting results to operational actions and measurable outcomes.

Benefits

Competitive salary and flexible benefits package.
Short-term incentive (STI).
Health insurance.
Dental insurance.
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 for eligible employees.