AI/Data Science Engineer II

MiniMed•Los Angeles, CA

1d•$87,000 - $149,000

About The Position

This role delivers big-data analytics and AI solutions to support business decision-making. It bridges applied data science and advanced AI to inform product design decisions and support quality reporting and investigations. The position combines Python and SQL expertise with statistical analysis and machine learning, while providing opportunities to explore GenAI-based solutions. The engineer works with engineers and scientists to turn complex product data into actionable insights and contributes to data infrastructure strategy.

Requirements

Bachelor's degree in Data Science, Computer Science, Engineering, Statistics, or a related field.
3–5 years of hands-on experience deriving insights and driving decisions from large, complex datasets, or 1–3 years with a master's degree.
Proficiency in Python, including pandas, NumPy, and data visualization libraries such as matplotlib, seaborn, and plotly.
Advanced SQL proficiency, including complex joins, window functions, CTEs, and query optimization.
Hands-on experience with data warehouse platforms such as Databricks, Snowflake, AWS Redshift, BigQuery, or Azure Synapse.
Working knowledge of foundational statistical concepts, including hypothesis testing, distributions, regression, and sampling methods, applied to product and quality datasets.
Ability to translate technical findings into clear, actionable insights for cross-functional audiences.
Familiarity with ETL/ELT workflows and pipeline tooling.
Experience with cloud platforms such as AWS, Azure, or GCP in a data context.

Nice To Haves

Experience or genuine interest in medical technology, clinical studies, or healthcare data.
Interest in turning complex data into compelling narratives that drive action.
Familiarity with advanced statistical techniques such as multivariate analysis, survival analysis, or statistical process control (SPC).
Experience with Power BI, Tableau, or similar dashboard and reporting platforms.
Familiarity with scikit-learn or similar machine learning frameworks for classification, regression, or anomaly detection.
Experience with AI-assisted coding environments such as Windsurf/Devin, GitHub Copilot, or similar tools.
Experience building production-level GenAI solutions to solve real-world problems.

Responsibilities

Partner with engineers and scientists to design and deliver analytical models, dashboards, and data pipelines that translate complex product datasets into actionable insights supporting product design decisions.
Use Python data analysis and visualization tools, including pandas, matplotlib, seaborn, and plotly, to develop scalable, reusable analytical solutions.
Design, build, and optimize SQL queries and data models across large relational and cloud data warehouse environments, including developing and maintaining ETL/ELT pipelines.
Explore and integrate AI-assisted development tools such as Windsurf/Devin and GitHub Copilot to improve development velocity and code quality.
Prototype machine learning models to support predictive analytics use cases.
Communicate findings to technical and non-technical stakeholders through visualizations, reports, and presentations.
Contribute to data infrastructure strategy, including schema design, data governance, and warehouse performance.

Benefits

Competitive salary and flexible benefits package.
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, including eligibility for the Short Term Incentive (STI).
401(k) plan with company match.
Short-term disability coverage.
Paid time off and holidays.
Employee Stock Purchase Plan.
Employee Assistance Program.
Eligible employees may also receive access to a Non-qualified Retirement Plan Supplement and Capital Accumulation Plan, subject to IRS minimum earnings requirements.