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Flexible 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 ...

Blend360

Associate Director of Data Science

Columbia, MD · On-site

$58K - $59K/yr

Lead the delivery of AI and data science projects, managing a team of 4-5 developers and data ... Domain knowledge in civil engineering, manufacturing, structural engineering, or mechanical ...

Blend360

Associate Director of Data Science

Columbia, MD · On-site +1

$58K - $59K/yr

Lead the delivery of AI and data science projects, managing a team of 4-5 developers and data ... Domain knowledge in civil engineering, manufacturing, structural engineering, or mechanical ...

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Flexible Data Science Civil Engineering information

What is a flexible data science civil engineering?

A Flexible Data Science Civil Engineering job combines the principles of civil engineering with data science techniques, allowing professionals to analyze complex infrastructure data, optimize project designs, and improve construction processes. The 'flexible' aspect typically refers to flexible work arrangements, such as remote work, part-time hours, or project-based roles. Professionals in this field use tools like machine learning, statistical analysis, and big data to inform decisions about transportation, water resources, structural engineering, and more. This role is ideal for those who are interested in leveraging data-driven insights to solve real-world engineering problems while enjoying adaptable work schedules.

What are the key skills and qualifications needed to thrive as a flexible data science civil engineer?

To thrive as a Flexible Data Science Civil Engineer, you need a solid background in civil engineering principles, statistics, and data analysis, typically supported by a relevant engineering degree. Proficiency with data analysis tools like Python, R, MATLAB, and civil engineering software such as AutoCAD or GIS platforms is highly valued, and certifications in data science or engineering can be beneficial. Strong problem-solving, communication, and adaptability skills set candidates apart, especially when translating complex data insights into practical engineering solutions. These competencies enable professionals to innovate, optimize infrastructure projects, and address real-world challenges effectively in a rapidly evolving field.

How does a flexible data science role in civil engineering typically collaborate with multidisciplinary teams on infrastructure projects?

In a Flexible Data Science Civil Engineering role, professionals often work closely with civil engineers, project managers, GIS specialists, and construction teams to analyze large datasets related to structural performance, materials, and site conditions. Collaboration usually involves translating complex data insights into actionable recommendations that inform design choices, project timelines, and risk assessments. Regular meetings, shared digital platforms, and data visualization tools are commonly used to ensure clear communication across disciplines, enabling data-driven decision-making throughout the project lifecycle.

What is the difference between Flexible Data Science Civil Engineering vs Structural Engineer?

AspectFlexible Data Science Civil EngineeringStructural Engineer
Required CredentialsBachelor's or master's in civil engineering, data science, or related fields; certifications in data analysis or civil engineeringBachelor's or master's in civil or structural engineering; PE license often preferred
Work EnvironmentDesigning data-driven solutions for civil projects, often in offices or on-siteDesigning, analyzing, and inspecting structural components, mainly in offices or construction sites
Industry UsageUsed in infrastructure projects, urban planning, and smart city initiativesPrimarily in building, bridge, and infrastructure design and safety assessments

Flexible Data Science Civil Engineering combines data analysis skills with civil engineering knowledge to develop innovative solutions. In contrast, Structural Engineers focus on designing and analyzing physical structures. Both roles require civil engineering credentials but differ in their focus—data-driven analysis versus structural design.

What cities are hiring for Flexible Data Science Civil Engineering jobs?

Cities with the most Flexible Data Science Civil Engineering job openings:

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

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

What states have the most Flexible Data Science Civil Engineering jobs?

States with the most job openings for Flexible Data Science Civil Engineering jobs include:

Flexible Data Science Civil Engineering jobs near you
Infographic showing various Flexible Data Science Civil Engineering job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 67% Full Time, 29% Part Time, and 3% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution.

Principal Data Scientist

CYNET SYSTEMS

Oakland, CA • On-site

$128 - $148/hr

Contractor

Re-posted 13 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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