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Internship Data Science Civil Engineering Jobs (NOW HIRING)

CYNET SYSTEMS

Principal Data Scientist

Oakland, CA · On-site

$128 - $148/hr

Master's Degree in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field. * Experience in Data Science ...

New

APTIM

Civil Engineering Intern

Concord, CA · On-site

$25 - $34.11/hr

Interns will be paired with experienced engineering and science staff who will provide training on ... Collaborate with current engineering staff in the interpretation of engineering data, conduct ...

Schmidt Associates

Civil Engineering Intern

Indianapolis, IN · On-site

$16.75 - $21.75/hr

Student Internship Opportunity - Civil Engineering We are currently seeking Civil Engineering ... Performing calculations and compiling data for engineering studies * Assisting with field ...

Thompson & Litton Inc

Civil Engineering Intern

Radford, VA · On-site

$16.50 - $22/hr

Join Thompson & Litton in our Radford office for a hands-on internship that puts you at the center ... You are currently enrolled in a Bachelor of Science program for Civil Engineering . * Communication:

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How much do internship data science civil engineering jobs pay per hour?

As of Jul 11, 2026, the average hourly pay for internship data science civil engineering in the United States is $19.85, according to ZipRecruiter salary data. Most workers in this role earn between $16.83 and $21.88 per hour, depending on experience, location, and employer.

What is the difference between Internship Data Science Civil Engineering vs Civil Engineering Intern?

AspectInternship Data Science Civil EngineeringCivil Engineering Intern
Required CredentialsBasic knowledge of data science, programming, civil engineering fundamentalsEnrolled in civil engineering degree, basic engineering coursework
Work EnvironmentData analysis, modeling, software tools, field visitsSite visits, design work, construction supervision
Employer & Industry UsageEngineering firms, government agencies, construction companiesConstruction firms, consulting agencies, government departments
Common Search & ComparisonInternship Data Science Civil EngineeringCivil Engineering Intern

The Internship Data Science Civil Engineering focuses on applying data analysis and modeling within civil engineering projects, often involving software tools and data-driven decision making. In contrast, a Civil Engineering Intern typically engages in site visits, design tasks, and construction supervision. Both roles serve as entry points into the civil engineering industry but emphasize different skill sets and work environments.

What are the key skills and qualifications needed to thrive as an Internship Data Science Civil Engineering, and why are they important?

To thrive as an intern in Data Science for Civil Engineering, you need a foundational understanding of civil engineering principles, basic statistics, and data analysis, often supported by ongoing or completed coursework in civil engineering or data science. Familiarity with programming languages like Python or R, knowledge of data visualization tools, and experience with software such as MATLAB or AutoCAD are typically required. Strong analytical thinking, attention to detail, and effective communication skills help interns interpret data and present findings clearly. These skills are essential for leveraging data-driven insights to solve engineering challenges and support project decision-making.

What are internship data science roles in civil engineering?

Internship data science roles in civil engineering involve using data analysis, machine learning, and statistical methods to solve problems in areas like construction, transportation, and infrastructure. Interns may work with large datasets from sensors, surveys, or simulations to help civil engineers make better decisions about design, safety, and efficiency. These roles often require knowledge of programming languages like Python or R, as well as an understanding of civil engineering principles. Interns gain practical experience by working on real-world projects, often supporting tasks such as predictive modeling, data visualization, and report generation.

What types of projects can I expect to work on during a Data Science internship in Civil Engineering?

As a Data Science intern in Civil Engineering, you may be involved in projects such as analyzing structural health monitoring data, optimizing transportation systems using predictive modeling, or automating data collection from construction sites. Interns often collaborate with engineers and data professionals to interpret large datasets, develop machine learning models, and create visualizations that support infrastructure planning and decision-making. The work environment is typically interdisciplinary, giving you valuable exposure to both technical data science tools and practical civil engineering applications.
More about Internship Data Science Civil Engineering jobs
What cities are hiring for Internship Data Science Civil Engineering jobs? Cities with the most Internship Data Science Civil Engineering job openings:
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What states have the most Internship Data Science Civil Engineering jobs? States with the most job openings for Internship Data Science Civil Engineering jobs include:
What job categories do people searching Internship Data Science Civil Engineering jobs look for? The top searched job categories for Internship Data Science Civil Engineering jobs are:
Internship Data Science Civil Engineering jobs near you
Infographic showing various Internship Data Science Civil Engineering job openings in the United States as of July 2026, with employment types broken down into 86% Full Time, 11% Part Time, 1% Temporary, and 2% Contract. Highlights an 92% Physical, 3% Hybrid, and 5% Remote job distribution, with an average salary of $41,288 per year, or $19.9 per hour.
Principal Data Scientist

Principal Data Scientist

SPECTRAFORCE TECHNOLOGIES Inc.

Oakland, CA • On-site

Other

Posted 4 days ago

New

Job description

Principal Data Scientist
12 months+ contract
Oakland, CA-Hybrid (one day per week onsite)
****Local Candidates Only****
Equipment: Client'' laptop will be provided upon start (or within a few days). If delayed, personal device may be used via Citrix/VDI
Top Skills:

  • Pyspark Proficiency
  • User Interface Development Proficiency
  • Strong Cross-Functional Collaboration Skills

Qualifications
Minimum:

  • Master’s Degree in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field.
  • Experience in Data Science, 8 years or 2 years experience, if possess Doctoral Degree or higher in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field.

Desired:

  • Doctorate Degree in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field.
  • Expertise in experimental design and causal inference methods.
  • Expertise in statistical methods for time series analysis, statistical modeling, and probabilistic risk assessment.
  • Relevant industry experience (electric or gas utility, data science consulting, etc.)
  • Familiarity with the use of supervised, unsupervised, deep learning & physics-based methods for modeling electrical infrastructure failure modes.
  • Competency with data science standards and processes (model evaluation, optimization, feature engineering, etc) along with best practices to implement them
  • Knowledge of industry trends and current issues in job-related area of responsibility as demonstrated through peer reviewed journal publications, conference presentations, open source contributions or similar activities
  • Competency with Agile product development best practices.
  • Proficiency with Python or Pyspark, code reviews, and code development best practices.
  • Proficiency in explaining in breadth and depth technical concepts including but not limited to statistical inference, machine learning algorithms, software engineering, model deployment pipelines.
  • Mastery in clearly communicating complex technical details and insights to colleagues and stakeholders
  • Ability to develop, coach, teach and/or mentor others to meet both their career goals and the organization goals

Position Summary:
Leads the design, development, and execution of scripts, programs, models, user interfaces, algorithms, and processes, using structured and unstructured data from disparate sources and sizes, generating for defensible, valid, scalable, reproducible and documented machine learning and artificial intelligence models (predictive or optimization) for problem solving and strategy development. Educates the non-technical community on advantages, risks, and maturity levels of data science solutions.
Job Responsibilities:

  • Researches and applies advanced knowledge of existing and emerging data science principles, theories, and techniques to inform business decisions.
  • Creates advanced data mining architectures / models / protocols, statistical reporting, and data analysis methodologies to identify trends in structured and unstructured data sets
  • Extracts, transforms, and loads data from dissimilar sources from across client for their machine learning feature engineering
  • Applies data science/ machine learning /artificial intelligence methods to develop defensible and reproducible predictive or optimization models that involve multiple facets and iterations in algorithm development.
  • Wrangles and prepares data as input of machine learning model development and feature engineering
  • Architects, develops, and documents reusable functions and modular code for data science.
  • Assesses business implications associated with modeling assumptions, inputs, methodologies, technical implementation, analytic procedures and processes, and advanced data analysis.
  • Works with stakeholder departments and company subject matter experts to understand application and potential of data science solutions that create value.
  • Presents findings and makes recommendations to senior management.
  • Act as peer reviewer of complex models.

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