Graduate Intern - Modeling and Data Science

Donaldson Company•Bloomington, MN

7d•Onsite

About The Position

The Modeling and Data Science Graduate Level Intern will help advance practical engineering and data solutions for filtration research and product development. Working with the Modeling and Data Science team, the intern will gain hands-on experience across engineering simulation, software development, data engineering, and data science by developing digital engineering capabilities that accelerate innovation and product development at Donaldson. The internship program offers real-world projects, mentorship, and development opportunities in a collaborative industry environment. This role is based on-site at Donaldson’s Bloomington, Minnesota facility. The position is overtime eligible, may require up to 5% travel, and is not eligible for relocation assistance. Applicants must be legally authorized to work in the United States without current or future immigration sponsorship. The hourly pay rate is $29.00–$32.00, depending on projected undergraduate graduation date, educational degree in progress, and major of study at the time of hire.

Requirements

Currently pursuing a graduate degree in mechanical engineering, aerospace engineering, computer science, data science, data engineering, applied mathematics, or a related technical field.
Coursework or project experience using Python for data analysis, numerical methods, machine learning, scientific computing, or application development.
Experience working with technical datasets and communicating methods and results through written documentation or presentations.
Must be a U.S. person: U.S. citizen, U.S. permanent resident, political asylee, or refugee.
Must be currently and legally authorized to work in the United States without the need for current or future sponsorship.

Nice To Haves

Graduate-level study or research experience in computational fluid dynamics, machine learning, scientific computing, or data engineering.
Experience with deep-learning methods for spatial or high-dimensional data, including designing neural network architectures and training models using structured datasets.
Experience with CFD software and flow-field data, including pressure, velocity, pressure-drop, or particle-transport results.
Experience with reduced-order modeling, surrogate modeling, regression, validation, uncertainty analysis, or model performance metrics.
Experience with SQL, Snowflake or another cloud data warehouse, Streamlit, data validation, and layered data pipelines.
Experience with CAD, parameterizing geometry, 3D scanning, or scan-to-CAD workflows.
Familiarity with Git, software architecture, or AI-assisted development tools such as GitHub Copilot.
Ability to gather requirements from engineers or laboratory users and convert them into a clear technical scope.
Strong problem-solving, technical communication, and collaboration skills, including the ability to work through an open-ended development problem.

Responsibilities

Own a scoped technical project from problem definition through development and final recommendations.
Build and evaluate Python-based engineering or data-science workflows using version-controlled, reproducible methods.
Prepare, clean, validate, and analyze simulation or laboratory datasets; document data quality issues and assumptions.
Develop and test models or applications such as deep-learning surrogates, reduced-order models, data-ingestion tools, or parameterized geometry workflows.
Develop a parameterized geometry workflow for simulation setup, simulation execution, and results evaluation.
Compare simulation results, laboratory results, or baseline engineering methods using appropriate technical metrics.
Collaborate with simulation engineers, data scientists, and subject-matter experts to interpret practical requirements.
Document architecture, methods, assumptions, and recommended next steps so the work can be extended after the internship.
Provide progress updates, incorporate technical feedback, and communicate findings to technical and non-technical stakeholders.
Follow Donaldson requirements for data access and security.