domain

Intern - Data Engineering - Corp

US St. Louis, MO, US
Onsite 2026-06-23
Estimated salary · St. Louis
~ $61,173 - $105,570
iampro estimate — the employer published no figure
Estimated net pay
$4,119 - $6,592
/month · 19% withheld
after tax & contributions · on the estimated salary · Single, no dependents

Job description

Based in St. Louis, Core \& Main is a leader in advancing reliable infrastructure™ with local service, nationwide®. As a specialty distributor with a focus on water, wastewater, storm drainage and fire protection products and related services, Core \& Main provides solutions to municipalities, private water companies and professional contractors across municipal, non\-residential and residential end markets, nationwide. With over 370 locations across the U.S., the company provides its customers local expertise backed by a national supply chain. Core \& Main’s 5,700 associates are committed to helping their communities thrive with safe and reliable infrastructure. Visit **coreandmain.com** to learn more. Job Summary The AI/ML Intern will support the Data Engineering team in developing and applying machine learning and artificial intelligence solutions that enhance business operations, analytics, and decision\-making. This role will assist in building data pipelines, experimenting with machine learning models, and contributing to AI\-driven initiatives such as forecasting, automation, and intelligent data processing. The intern will work closely with data engineers, analytics teams, and business stakeholders to deliver proof\-of\-concept solutions and support the organization’s growing AI/ML capabilities. Qualifications Minimum Qualifications * Currently pursuing a Bachelor’s or Master’s degree in: + Computer Science + Data Science + Engineering + Mathematics or a related field * Basic understanding of programming (Python preferred) * Foundational knowledge of: + Statistics and machine learning concepts + Data analysis techniques Preferred Qualifications * Experience with: + Python libraries (Pandas, NumPy, scikit\-learn, PySpark) + SQL and data querying + Cloud platforms (Azure preferred) + Copilot Agents + Data visualization tools (Power BI or Tableau) * Exposure to: + Machine learning models (regression, classification, clustering) + T