Graduate Intern - Modeling and Data Science at Donaldson in Bloomington, MN

Job description

Donaldson is committed to solving the world’s most complex filtration challenges. Together, we make cool things. As an established technology and innovation leader, we are continuously evolving to meet the filtration needs of our changing world. Join a culture of collaboration and innovation that matters and a chance to learn, effect change, and make meaningful contributions at work and in communities. ​ 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 graduate level intern will gain valuable hands-on experience at the intersection of engineering simulation, software development, data engineering, and data science by developing next-generation digital engineering capabilities that help accelerate innovation and product development at Donaldson. The Donaldson internship program provides innovative students with meaningful, hands-on experience in a collaborative industry environment. Through real-world projects, mentorship, and development opportunities, we aim to support career growth and build professional talent aligned with our values and goals. Qualified candidates must be able to work an on-site schedule at our Bloomington, MN facility. Role Responsibilities: Own a scoped technical project from problem definition to 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 or baseline engineering methods. 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 both technical and non-technical stakeholders. Follow Donaldson requirements for data access and security. Travel : Up to 5%. Minimum Qualifications: 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 U.S. persons (U.S. Citizen, U.S. Permanent Resident (i.e., 'Green Card Holder'), Political Asylee, or Refugee). Preferred Qualifications: Graduate-level study or research experience in computational fluid dynamics, machine learning, scientific computing, or data engineering. Experience or project work in any subset of these areas: Deep-learning methods for spatial or high-dimensional data, including designing neural networks architectures and training models using structured datasets. CFD software and flow-field data, including pressure, velocity, pressure-drop, or particle-transport results. 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. CAD, parameterizing geometry, 3D scanning, or scan-to-CAD workflows. Familiarity with Git, software architectur