Data Scientist at Quanta Services, Englewood, CO
Data Scientist at Quanta Services in Englewood, CO. Full-time. $145,000 to $165,000 a year. Apply with swipeandhired.
Regular
Location
US-CO-Englewood
Workplace Type
On-Site
About Us
PAR Electrical Contractors, LLC is a premier outside electrical infrastructure construction company based in Kansas City, Missouri. A subsidiary of Quanta Services, Inc. (NYSE:PWR), PAR is one of the founding members of Quanta Services and among the largest union contractors in the U.S. PAR’s employees are the reason for the success of our company. Our reputation attracts the highest quality personnel, people who are committed to producing projects that set a standard for excellence. At PAR, we take on the most challenging and complex projects we can find, and we hire people who thrive on hard work and demanding opportunities.
About this Role
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.
**Up to 10% Travel required - mainly Monday through Friday**
Salary: $145k - $165k annual based on experience
What You'll Do
- 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 loose ly 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.
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