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Data Science Civil Engineering Jobs in California

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 ...

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 ...

Adidev Technologies Inc

Data Science

San Francisco, CA · On-site

As a Data Scientist/Data Science Specialist for Adidev Technologies Inc., you will be enhancing and ... Strong foundation in Python programming in a cloud environment. * Strong quantitative abilities ...

Adidev Technologies Inc

Data Science

San Francisco, CA · Remote

As a Data Scientist/Data Science Specialist for Adidev Technologies Inc., you will be enhancing and ... Strong foundation in Python programming in a cloud environment. * Strong quantitative abilities ...

W3Global Inc.

Civil Engineering

South Lake Tahoe, CA

$96K - $120K/yr

... civil engineering projects. The ideal candidate will have experience in restoration design ... GIS mapping and data analysis experience * Master's Degree in a related field * QSD or QSP ...

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Data Science Civil Engineering information

See California salary details

$25K

$101.1K

$191.9K

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

As of Aug 17, 2026, the average yearly pay for data science civil engineering in California is $101,141.00, according to ZipRecruiter salary data. Most workers in this role earn between $59,732.00 and $133,664.00 per year, depending on experience, location, and employer.

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.

What are the typical daily responsibilities of a data science civil engineering professional?

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.

What are the key skills and qualifications needed for a data science civil engineering position?

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.

Can a data science civil engineer become a data scientist?

A data science civil engineer can become a data scientist by acquiring relevant skills such as programming, statistical analysis, and machine learning, often through additional training or certifications. Their engineering background provides a strong foundation in problem-solving and data analysis, which are valuable in data science roles.

What are the most commonly searched types of Data Science Civil Engineering jobs in California?

The most popular types of Data Science Civil Engineering jobs in California are:

What are popular job titles related to Data Science Civil Engineering jobs in California?

For Data Science Civil Engineering jobs in California, the most frequently searched job titles are:

What job categories do people searching Data Science Civil Engineering jobs in California look for?

The top searched job categories for Data Science Civil Engineering jobs in California are:

What cities in California are hiring for Data Science Civil Engineering jobs?

Cities in California with the most Data Science Civil Engineering job openings:

Data Science Civil Engineering jobs near you
Infographic showing various Data Science Civil Engineering job openings in California as of August 2026, with employment types broken down into 79% Full Time, 14% Part Time, and 7% Temporary. Highlights an 86% In-person, 7% Hybrid, and 7% Remote job distribution, with an average salary of $101,141 per year, or $48.6 per hour.

Principal Data Scientist

CYNET SYSTEMS

Oakland, CA • On-site

$128 - $148/hr

Contractor

Re-posted 11 days ago

Job description

Job Overview:

Pay Range: $128.66hr - $148.45hr

Requirement/Must Have:

  • 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 possessing Doctoral Degree or higher in a related field.

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 for machine learning feature engineering.
  • Applies data science/machine learning/artificial intelligence methods to develop defensible and reproducible predictive or optimization models.
  • Wrangles and prepares data as input for 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 advanced data analysis.
  • Works with stakeholder departments and subject matter experts to understand application and potential of data science solutions.
  • Presents findings and makes recommendations to senior management.
  • Acts as peer reviewer of complex models.

Nice to Have:

  • 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.
  • Knowledge of industry trends and current issues in job-related area of responsibility.
  • Competency with Agile product development best practices.
  • Proficiency with Python or PySpark, code reviews, and code development best practices.
  • Proficiency in explaining technical concepts including 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.

Skills:

  • Pyspark proficiency.
  • User interface development proficiency.
  • Strong cross-functional collaboration skills.
 

Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading staffing and recruiting powerhouse. Proudly recognized as a nationally and locally certified diversity firm, Cynet delivers agile, scalable talent solutions across industries. With an active footprint in all 50 U.S. states and Canada, we support thousands of consultants through our expansive, high-performing recruitment engine operating across North America and Asia—ensuring speed, quality, and consistency in every hire.
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About Cynet Systems

Sourced by ZipRecruiter

Cynet Systems Inc is a staffing and recruiting corporation nestled in Ashburn, VA, USA. Established in 2010, the company operates within the Information Technology and Services sector, specializing in providing effective workforce solutions to different business needs, including IT consulting, direct hire, and contract staffing services. Through the years, Cynet Systems has built an impressive portfolio, going beyond borders and expanding its operations internationally in Canada and India. Rooted in its core values of teamwork, leadership, and commitment, Cynet Systems helps businesses unlock their full potential by providing versatile and competent professionals that perfectly align with their needs. Fueled by their unwavering mission to deliver top-tier talent to businesses worldwide, Cynet Systems garnered various recognitions including SIA's fastest-growing staffing firms and Best Place to Work in Virginia for 2019.

Industry

It services

Company size

501 - 1,000 Employees

Headquarters location

Sterling, VA, US

Year founded

2010

Social media

Twitter

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