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Afternoon 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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Afternoon Data Science Civil Engineering information

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$51.5K

$147.5K

$197K

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

As of Jul 10, 2026, the average yearly pay for afternoon data science civil engineering in the United States is $147,461.00, according to ZipRecruiter salary data. Most workers in this role earn between $84,000.00 and $196,000.00 per year, depending on experience, location, and employer.

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

Civil engineers typically earn salaries below $200,000 annually, with higher earnings possible for senior roles, project managers, or those working in specialized fields or high-cost regions. Achieving a $500,000 salary usually requires extensive experience, advanced certifications, leadership positions, or consulting work, which are less common in standard civil engineering roles.

What engineer makes $500,000 a year?

In the field of civil engineering, very few professionals earn $500,000 annually; such high salaries are typically associated with executive roles, consulting firm owners, or engineers with extensive experience and specialized expertise. Data scientists and certain senior engineers in high-demand industries may reach this level, especially with bonuses and profit sharing, but it is uncommon for standard civil engineers to do so.

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

AspectAfternoon Data Science Civil EngineeringAfternoon Data Science Structural Engineering
CredentialsDegree in Civil Engineering, Data Science certificationsDegree in Structural Engineering, Data Science certifications
Work EnvironmentConstruction sites, urban planning projectsDesign firms, building analysis environments
Industry UsageInfrastructure, transportation, urban developmentBuilding design, safety analysis, material testing

Both roles involve applying data science to engineering projects, but Civil Engineering focuses on infrastructure and urban projects, while Structural Engineering emphasizes building stability and safety. The required credentials and work environments overlap significantly, making them closely related but distinct specialties within engineering data science.

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, but they typically need to learn tools like Python, R, and data visualization software to transition successfully into data science roles.

Is 30 too late for data science?

For a data science civil engineering role, starting a career at age 30 is not too late, as many professionals transition into data analysis or engineering roles later in life. Success depends on acquiring relevant skills such as programming, statistics, and domain knowledge, which can be developed through online courses, certifications, or degrees. Experience, continuous learning, and practical application are key factors regardless of age.
What cities are hiring for Afternoon Data Science Civil Engineering jobs? Cities with the most Afternoon 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 Afternoon Data Science Civil Engineering jobs? States with the most job openings for Afternoon Data Science Civil Engineering jobs include:
Afternoon Data Science Civil Engineering jobs near you
Principal Data Scientist

Principal Data Scientist

SPECTRAFORCE TECHNOLOGIES Inc.

Oakland, CA • On-site

Other

Posted 3 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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