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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 ...

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Data Science Civil Engineering information

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$25.5K

$103.3K

$196K

How much do data science civil engineering jobs pay per year?

As of Jul 13, 2026, the average yearly pay for data science civil engineering in the United States is $103,286.00, according to ZipRecruiter salary data. Most workers in this role earn between $61,000.00 and $136,500.00 per year, depending on experience, location, and employer.

How is data science used in civil engineering?

Data science in civil engineering involves analyzing large datasets to optimize design, construction, and maintenance of infrastructure. Civil engineers use data analytics, machine learning, and modeling tools to improve project efficiency, predict structural performance, and manage resources effectively.

What is a Data Science Civil Engineering job?

A Data Science Civil Engineering job combines data science techniques with civil engineering expertise to analyze, model, and optimize infrastructure projects. Professionals in this field use machine learning, statistical analysis, and big data tools to improve structural design, traffic systems, and environmental sustainability. They work with large datasets from sensors, GIS systems, and simulations to enhance decision-making in construction, transportation, and urban planning.

Can you make $500,000 as a civil engineer?

Data science civil engineering is a specialized field combining data analysis with civil engineering. While high salaries are possible for experienced professionals in senior roles, reaching $500,000 typically requires extensive experience, advanced skills, leadership positions, or working in high-cost regions or consulting firms. Most civil engineers earn lower base salaries, but additional compensation through bonuses or profit sharing can increase total earnings.

Can a civil engineer become a data scientist?

A civil engineer can become a data scientist by acquiring skills in programming, statistics, and machine learning, often through additional education or training. Their background in problem-solving and data analysis can be advantageous, especially when working with engineering or infrastructure data using tools like Python, R, or SQL.

What engineers make $500,000?

Senior civil engineers with extensive experience, specialized skills, and leadership roles can earn salaries approaching or exceeding $500,000 annually, especially in consulting or managerial positions. Achieving this level often requires advanced certifications, a strong track record, and working in high-demand or high-cost regions.

What are the key skills and qualifications needed to thrive in the Data Science Civil Engineering position, and why are they important?

To excel in Data Science Civil Engineering, you need a strong background in civil engineering principles along with expertise in data analytics, statistical modeling, and programming languages such as Python or R. Familiarity with tools like MATLAB, GIS software, machine learning libraries, and certifications in data science or engineering analysis is often required. Strong problem-solving abilities, communication skills, and the capacity to work collaboratively with interdisciplinary teams distinguish top professionals in this field. These skills are crucial for driving data-informed decisions, optimizing engineering designs, and effectively addressing complex infrastructure challenges.

What are typical daily responsibilities for someone working in Data Science within Civil Engineering?

Professionals in Data Science Civil Engineering routinely analyze large datasets related to construction, infrastructure performance, and environmental variables to inform design and maintenance decisions. Daily tasks may include developing predictive models, preparing visualization dashboards, interpreting sensor data, and collaborating with civil engineers to translate data insights into actionable project recommendations. The role often requires close teamwork with engineering, IT, and project management staff to ensure that analytical findings align with project goals and regulatory requirements. This blend of technical and collaborative work makes each day dynamic and impactful, contributing directly to improved safety, efficiency, and sustainability in civil engineering projects.

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Data Science Civil Engineering jobs near you
Principal Data Scientist

Principal Data Scientist

SPECTRAFORCE TECHNOLOGIES Inc.

Oakland, CA • On-site

Other

Posted 6 days ago

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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