AI/Data Science Engineer II

MiniMed•Minneapolis, MN

16h

About The Position

The AI/Data Science Engineer II delivers big-data analytics and AI solutions that support business decision-making. The role bridges applied data science and advanced AI to inform product design decisions and provide robust quality reporting and investigation support. It partners with engineers and scientists to turn complex product datasets into actionable insights through analytical models, dashboards, and data pipelines. The position also contributes to data infrastructure strategy, including schema design, data governance, and warehouse performance, and communicates findings to technical and non-technical stakeholders.

Requirements

Bachelor’s degree and at least 2 years of relevant experience, or an advanced degree with 0 years of experience.
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.
Solid working knowledge of foundational statistics, 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.

Nice To Haves

Bachelor’s degree in Data Science, Computer Science, Engineering, Statistics, or a related field.
Three to five years of hands-on experience deriving insights and driving decisions from large, complex datasets, or one to three years if holding a master’s degree.
Hands-on experience with data warehousing platforms such as Databricks, Snowflake, AWS Redshift, BigQuery, or Azure Synapse.
Experience with AWS, Azure, or GCP in a data context.
Experience or genuine interest in medical technology, clinical studies, or healthcare data.
Interest in data storytelling and turning complex data into 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-based 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-scale relational and cloud data warehouse environments.
Develop and maintain 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 compensation and flexible benefits package.
Health, dental, and vision insurance.
Health Savings Account and Healthcare Flexible Spending Account.
Life insurance and long-term disability leave.
Dependent daycare spending account.
401(k) plan with company match.
Short-term disability coverage.
Paid time off and holidays.
Employee Stock Purchase Plan.
Employee Assistance Program.
Short-term incentive plan eligibility.
Eligible employees may also qualify for a Non-qualified Retirement Plan Supplement and Capital Accumulation Plan, subject to IRS minimum earnings requirements.