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# AI/Data Science Engineer II

- **Company:** MINIMED LLC
- **Location:** Los Angeles, CA, United States
- **Salary:** $87,000.0 - $149,000.0
- **Contract:** Temporary contract
- **Skills:** Sql Data Warehouse, Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Big Data, BigQuery, Cloud Computing, Software Quality, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Presentation, Data Visualization, Data Warehousing, Decision Support Systems, Programming Tools, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, NumPy, Query Optimization, Power BI, Tensorflow, Standard Sql, Statistical Process Control (SPC), SQL Databases, Computational Statistics, Tableau (Software), Google Cloud, GitHub Copilot, Snowflake, Pandas, Matplotlib, Information Technology, Data Analytics, Plotly, Meditech, Azure Synapse Analytics, Data Pipelines, Amazon Redshift, Databricks
- **Published:** July 1, 2026
- **Apply:** https://dejobs.org/x/x/3511FF3392E34EDA963444B0685E8E44/job/

## About the Role

Requires a Bachelor's Degree and minimum of 2 years of relevant experience, or advanced degree with 0 years of experience., * Education: Bachelor's degree in Data Science, Computer Science, Engineering, Statistics, or related field

 * Experience: 3-5 years (1-3 years if master's degree) of hands-on experience deriving insights and driving decisions from large, complex datasets
 * SQL: Advanced proficiency - complex joins, window functions, CTEs, query optimization
 * Data Warehousing: Hands-on experience with platforms such as Snowflake, Databricks, BigQuery, Redshift, or Azure Synapse
 * Python: Proficient in pandas, NumPy, and data visualization libraries (matplotlib, seaborn, plotly)
 * Statistics: Solid working knowledge of foundational statistical concepts - hypothesis testing, distributions, regression, and sampling methods - applied to real-world product and quality datasets
 * Communication: Ability to translate technical findings into clear, actionable insights for cross-functional audiences
 * Data Pipelines: Familiarity with ETL/ELT workflows and pipeline tooling
 * Cloud: Experience with cloud platforms (AWS, Azure, GCP) in a data context
 * MedTech / Healthcare: Experience or genuine interest in medical technology, clinical studies, or healthcare data - strongly preferred
 * Data Storytelling: A passion for turning complex data into compelling narratives that drive action - strongly preferred
 * Advanced Statistics: Familiarity with advanced statistical techniques such as multivariate analysis, survival analysis, or statistical process control (SPC)
 * AI Dev Tools: Experience with AI-assisted coding environments such as Windsurf, GitHub Copilot, or similar
 * ML/Modeling: Familiarity with scikit-learn or similar ML frameworks for classification, regression, or anomaly detection
 * BI Tools: Experience with Power BI, Tableau, or similar dashboard/reporting platforms

## Description

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.

Responsibilities may include the following and other duties may be assigned.

 * 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


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