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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 are the key skills and qualifications needed to thrive as a Flexible Data Science Civil Engineer, and why are they important?

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.

What engineers make $500,000?

Highly experienced civil engineers, especially those in senior management or specialized consulting roles, can earn $500,000 or more annually. Achieving this level often requires advanced skills, extensive experience, and leadership responsibilities, sometimes supplemented by bonuses or profit sharing.

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

Senior civil engineers with extensive experience, specialized skills, and leadership roles can potentially earn $500,000 or more annually, especially in high-demand industries or managerial positions. However, typical salaries for civil engineers generally range lower, and reaching this level often requires advanced certifications, a strong track record, and working in lucrative sectors or locations.

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.

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 a Flexible Data Science Civil Engineering job?

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.

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 engineering and data analysis can provide a strong foundation for transitioning into data science roles, especially when combined with knowledge of data tools like Python, R, and SQL.

Is 40 too late for data science?

In data science roles within civil engineering, starting a career at 40 is feasible as skills in programming, statistics, and domain knowledge are valuable regardless of age. Many professionals successfully transition into data science later in life by gaining relevant certifications and experience. Age should not be a barrier if you develop the necessary technical skills and stay current with industry tools like Python, R, and data visualization software.
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 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.
Principal Data Scientist

Principal Data Scientist

SPECTRAFORCE TECHNOLOGIES Inc.

Oakland, CA • On-site

Other

Posted 20 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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