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# Data Scientist

- **Company:** PAR LLC
- **Location:** Greenwood Village, CO, United States
- **Experience:** Experienced
- **Salary:** $145,000.0 - $165,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Cloud Database, Software Quality, Databases, Extract Transform Load (ETL), Document-Oriented Databases, Python (Programming Language), Machine Learning, Cloud Services, Standard Sql, Model Validation, Data Analytics, Databricks
- **Published:** October 29, 2026
- **Apply:** https://www.careerbuilder.com/job-details/data-scientist-englewood-co--c91448c7-b002-4ab2-a1a0-d36c967fc175

## About the Role

Data Science, Electricity, Infrastructure Construction, Artificial Intelligence (AI), Industrial Engineering, Data Science, Machine Learning, Product Development, Willing to Travel, Database Extract Transform and Load (ETL), * Master's degree + 4-6 years of experience, OR a Bachelor's degree + 6+ years of relevant experience required.

 * Preferred certifications: Databricks Certified Machine Learning Professional, Certified Analytics Professional (CAP), or AWS ML Specialty.

 * Strong Python and SQL skills, with experience building predictive models that ran in production.

 * Very high attention to detail.

 * Excellent problem-solving skills.

 * Ability to work independently but as part of a team.

 * Experience in construction, industrial operations, engineering, or another asset-heavy, project-based industry preferred.

 * Familiarity with project financial or job-costing systems (EcoSys, JDE, or similar) or cloud data platforms such as Databricks preferred.

 * Experience as an early or first data science hire, building an analytics function rather than joining an established one, preferred.

 * Background in geophysics, subsurface or terrain risk modeling, or geospatial analysis preferred.

## Description

PAR Electric is seeking a highly motivated Data Scientist to own the design and execution of complex predictive, optimization, and machine learning models. This role works independently, reporting to the Director of AI & Machine Learning, building data products that solve intricate operational, financial, and heavy industrial engineering challenges inherent to large-scale utility contracting., * Design, build, and optimize core cloud-based data pipelines and ETL processes tailored for utility infrastructure analytics.

 * Partner with project managers and operations teams to identify efficiency gaps and deliver data-driven solutions.

 * Work independently from a loosely defined business question through to a finished analysis or model, without an existing team or pipeline to lean on.

 * Build and validate models on real operational and financial data, including data that is incomplete, unlabeled, or inconsistently recorded.

 * Validate models against real project outcomes over time, beyond backtesting on a static dataset.

 * Communicate results and their limits clearly to non-technical stakeholders in operations, finance, and leadership.

 * Design and implement complex machine learning, industrial optimization, and forecasting models.

 * Establish best practices for code quality, model validation, and deployment.

 * Interface with executive stakeholders to present findings and recommendations, as well as align data science capabilities with operational strategies.

 * Collaborate with multi-disciplinary teams to document data pipelines and workflows.

 * Design intuitive charts, dashboards, and reports that clearly highlight trends, risks, and actionable operational insights.

 * Break down complex, unstructured industrial and logistical problems into logical, data-driven components.

 * Prioritize multiple data requests and model iterations across different active construction sites or project phases.

 * Follow strict scientific and statistical protocols to ensure models are reliable, reproducible, and compliant with engineering standards.

 * Work effectively across siloed departments, such as field operations, corporate procurement, and design engineering teams.

 * Ensure high-quality, clean, and mathematically sound datasets.

 * All other duties assigned by management