AI/Data Science Engineer II

MiniMed•Los Angeles, CA

2d•$87,000 - $149,000

About The Position

This AI/Data Science Engineer II role is focused on delivering big-data analytics solutions that drive decision support for the business. This role bridges advanced data engineering with applied data science to enable smarter product design decisions and robust quality reporting and investigation support. The ideal candidate combines deep SQL expertise, proficiency in modern data warehouse technologies, and strong Python-based data analysis skills, along with a growing interest in AI-augmented development tools.

Requirements

Requires a Bachelor's Degree and minimum of 2 years of relevant experience, or advanced degree with 0 years of experience.
Advanced proficiency in SQL - complex joins, window functions, CTEs, query optimization
Hands-on experience with data warehousing platforms such as Snowflake, Databricks, BigQuery, Redshift, or Azure Synapse
Proficient in Python - pandas, NumPy, and data visualization libraries (matplotlib, seaborn, plotly)
Solid working knowledge of foundational statistical concepts - hypothesis testing, distributions, regression, and sampling methods - applied to real-world 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 (AWS, Azure, GCP) in a data context

Nice To Haves

Bachelor's degree in Data Science, Computer Science, Engineering, Statistics, or related field
3-5 years (1-3 years if master’s degree) of hands-on experience deriving insights and driving decisions from large, complex datasets
Experience or genuine interest in medical technology, clinical studies, or healthcare data - strongly preferred
A passion for turning complex data into compelling narratives that drive action - strongly preferred
Familiarity with advanced statistical techniques such as multivariate analysis, survival analysis, or statistical process control (SPC)
Experience with AI-assisted coding environments such as Windsurf, GitHub Copilot, or similar
Familiarity with scikit-learn or similar ML frameworks for classification, regression, or anomaly detection
Experience with Power BI, Tableau, or similar dashboard/reporting platforms

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
Design, build, and optimize SQL-based queries and data models across large-scale relational and cloud data warehouse environments, including ETL/ELT pipeline development and maintenance
Apply Python-based data analysis and visualization tools (pandas, matplotlib, seaborn, plotly, etc.) to develop scalable, reusable analytical solutions
Explore and integrate AI-assisted development tools (e.g., Windsurf, GitHub Copilot) to improve development velocity and code quality, and prototype machine learning models to support predictive analytics use cases.
Communicate findings clearly to both technical and non-technical stakeholders through polished visualizations, reports, and presentations.
Contribute to data infrastructure strategy, including schema design, data governance, and warehouse performance