The Start-up, Commissioning & Optimization (SCO) group in the Water Quality & Treatment (WQ&T) section is seeking a Data Scientist to support process optimization studies and other projects by extracting meaningful insights from complex, large-scale datasets and delivering scalable, data-driven solutions that enhance business performance.
Requirements
- Utilize programming languages such as Python, R, SQL, and tools like Model Builder to automate data processing, perform analysis, and visualize results.
- Leverage complex, cross-functional datasets to develop data-driven solutions for water quality and treatment challenges such as treatability, chemical optimization, remote plant operations, and system monitoring.
- Develop dashboards and visualizations to support decision making, compliance and non-compliance reporting, capital planning, and asset management.
- Experience integrating data from multiple platforms, such as remote sensing, SCADA, LIMS, and GIS.
- Ability to develop predictive models to improve process performance and mitigate risk of water quality events.
- In-depth knowledge of treatment processes and associated water quality parameters.
- Experience analyzing water quality across reservoirs, treatment facilities, and/or distribution systems and improving monitoring strategies
Responsibilities
- Applies statistical analysis, machine learning, and data engineering techniques to extract meaningful insights from complex, large-scale datasets.
- Developing, validating, and deploying predictive models to support machine learning and artificial intelligence initiatives.
- Contributes to the design and optimization of data pipelines, feature engineering processes, and model evaluation frameworks, ensuring robust and reproducible results.
- Identify and correct data quality issues such as missing, incorrect, incomplete, or inaccurate information to ensure clean and reliable datasets for analysis.
- Collect, process, and analyze structured and unstructured data to derive actionable insights and support data-driven problem solving.
- Apply statistical methods, predictive modeling, and data mining techniques to forecast outcomes and assess model performance.
- Design and implement automated tools and workflows that generate intuitive data visualizations, maps and dashboards to support data interpretations and decision making.
Other
- Bachelor’s Degree from an accredited college or university in Statistics, Applied Mathematics, Engineering, Computer Science, Economics, or related field.
- Minimum 4 years of related work experience.
- Must be able to possess and maintain a valid Class ‘R’ Colorado driver’s license and have a satisfactory driving record at time of hire.
- Cover letter preferred
- Potential work from home opportunities
