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Contractual 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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Contractual Data Science Civil Engineering information

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$51.5K

$147.5K

$197K

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

As of Jul 22, 2026, the average yearly pay for contractual 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.

What engineers make $300,000 a year?

Senior civil engineers with extensive experience, specialized skills, and leadership roles can earn $300,000 or more annually, especially in large infrastructure projects or consulting firms. Data science professionals with engineering backgrounds working in high-demand industries or with advanced certifications may also reach this level, often combining technical expertise with managerial responsibilities.

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 in data analysis tools like Python, R, and SQL. Transitioning may also involve gaining experience with data visualization and working with large datasets to analyze engineering-related problems.

What is the difference between Contractual Data Science Civil Engineering vs Contractual Data Science Mechanical Engineering?

AspectContractual Data Science Civil EngineeringContractual Data Science Mechanical Engineering
Required CredentialsData Science certifications, Civil Engineering background, relevant software skillsData Science certifications, Mechanical Engineering background, relevant software skills
Work EnvironmentConstruction sites, urban planning projects, infrastructure developmentManufacturing plants, product design, machinery optimization
Employer & Industry UsageConstruction firms, government agencies, infrastructure companiesManufacturers, automotive, aerospace, industrial firms

Both roles involve applying data science skills within engineering contexts, but Civil Engineering focuses on infrastructure and construction projects, while Mechanical Engineering emphasizes manufacturing and machinery. The choice depends on the industry and project type you are interested in.

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. Data science roles in engineering firms may also reach high compensation levels when combined with advanced technical expertise and project management responsibilities.

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

Contractual data science civil engineers typically do not earn $500,000 annually, as salaries for civil engineering roles generally range lower, depending on experience, location, and project scope. High earnings may be possible for senior or specialized roles, especially with extensive experience, advanced certifications, or leadership positions, but such salaries are uncommon in standard civil engineering careers.
What cities are hiring for Contractual Data Science Civil Engineering jobs? Cities with the most Contractual 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 Contractual Data Science Civil Engineering jobs? States with the most job openings for Contractual Data Science Civil Engineering jobs include:
Contractual Data Science Civil Engineering jobs near you
Principal Data Scientist

Principal Data Scientist

SPECTRAFORCE TECHNOLOGIES Inc.

Oakland, CA • On-site

Other

Posted 14 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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