Paid AI & Data Engineering Intern for Smart Irrigation

Valmont Industries

Omaha (NE)

On-site

USD 25,000 - 39,000

Part time

4 days ago
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Benefits offered by this job

Paid internship
Tuition assistance
Part-time during academic year
Full-time in summer

Job summary

Valmont is seeking an AI and Data Engineering Intern in the Agriculture Division Software Engineering Department in Omaha, NE. You will work on data-driven decision-making, cloud data systems, predictive modeling, and cross-functional collaboration to deliver actionable data insights.

You’ll gain hands-on experience with Azure-based data pipelines, feature engineering, and ML model development, with mentorship and real project exposure.

Qualifications

  • Pursuing a degree in Data Science, CS, Statistics, Engineering or related field.
  • Proficient in SQL for data extraction and manipulation.
  • Strong Python skills (Pandas, NumPy, scikit-learn). Familiar with Azure.

Responsibilities

  • Extract, clean, and preprocess data from Azure-based databases.
  • Perform EDA to identify trends and opportunities.
  • Engineer features for predictive modeling and decision-making.
  • Develop and evaluate ML models for irrigation optimization.
  • Document methods and present insights to stakeholders.
  • Collaborate with engineering and product teams on data solutions.

Skills

SQL
Python (Pandas)
Azure
Data cleaning
Time-series data
Geospatial analysis
EDA / modeling
Visualization (Power BI/Tableau)

Education

Pursuing data science or related
Strong programming background

Tools

Power BI
Tableau
Plotly Dash
GeoPandas

Job description

Valmont is seeking an AI and Data Engineering Intern in the Agriculture Division Software Engineering Department in Omaha, NE. You will work on data-driven decision-making, cloud data systems, predictive modeling, and cross-functional collaboration to deliver actionable data insights.

You’ll gain hands-on experience with Azure-based data pipelines, feature engineering, and ML model development, with mentorship and real project exposure.

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