Applied Data Scientist, Operations Analytics at Rowan

Rowan

Denver, CO Office·10h ago

Mid LevelFull-timePythonData ScienceGit

Salary Range

$80,750 - $109,250 /year

Estimated

This salary is estimated based on similar roles. The actual salary may vary.

About the Role

The Applied Data Scientist, Operations Analytics at Rowan Digital Infrastructure develops statistically rigorous operational metrics and quantitative models to support reliability, maintenance optimization, and risk-aware decision making. This role focuses on applied modeling and indicator development, working closely with Operations, Reliability, Engineering, and Analytics teams to translate operational data into actionable insights.

Key Responsibilities

→Design and develop operational metrics and quantitative models that improve reliability, maintenance strategy effectiveness, and risk mitigation
→Apply statistical analysis, probability modeling, and optimization techniques to operational datasets to generate predictive indicators
→Quantify and model human error trends and operational performance variability using structured analytical frameworks
→Validate model assumptions, evaluate performance, document methodologies, and ensure alignment with analytics governance standards
→Translate analytical outputs into practical operational recommendations and present findings to both technical and non-technical stakeholders
→Partner with Operations, Reliability, Engineering, and IT teams to align modeling initiatives with operational realities
→Prepare and analyze data from operational systems and structured databases using SQL and analytics tools
→Continuously refine metrics, models, and analytical methodologies to improve reliability outcomes

Requirements

✓Bachelor’s or Master’s degree in Mathematics, Statistics, Data Science, Operations Research, Engineering, or a related quantitative field
✓Three or more years of experience in applied data science, quantitative analytics, operations research, or statistical modeling
✓Strong foundation in statistics, probability, and quantitative modeling
✓Experience developing operational metrics and predictive or optimization models in real-world environments
✓Proficiency with SQL and experience using analytics tools such as Python, R, Tableau, or similar platforms
✓Ability to work with complex operational datasets and clearly document and communicate analytical results