Applied Data Scientist, Operations Analytics

Rowan Digital Infrastructure • Denver, CO

5d • $105 - $125 • Hybrid

About The Position

Rowan Digital Infrastructure is redefining how data centers are delivered - faster, smarter, and at scale. We partner with hyperscale customers to provide tailored, high performance infrastructure with a focus on sustainability, efficiency, and flexibility Our experienced, end-to-end team delivers custom solutions across a growing portfolio of strategic sites in key markets across the United States. Backed by Quinbrook Infrastructure Partners, Rowan is committed to enabling the next generation of digital infrastructure—and building a more sustainable future in the process. Ready to help transform how the world’s most important technologies are powered? Join us. Role Summary The Applied Data Scientist, Operations Analytics reports to the Senior Manager, Operations Analytics and develops statistically rigorous operational metrics and quantitative models that support reliability, maintenance optimization, and risk aware decision making. This role focuses on applied modeling and indicator development, including quantifying human factor performance trends, and partners closely with Operations, Reliability, Engineering, and Analytics teams to translate operational data into defensible and actionable insights. The position supports predictive maintenance governance and continuous improvement within the Operations Analytics function. Travel: Ability to travel approximately 30% for customer meetings, company gatherings, and project sites. Location: Denver, CO (3 day hybrid in-office role) preferred, would consider remote for an exceptional candidate Compensation: $105-$125K (Offers Bonus)

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

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

Benefits

• Hybrid working environment
• Team building and educational opportunities
• Generous benefits package including robust health benefits and a 401(k) company contribution
• Unlimited PTO
• Competitive compensation and bonus plan

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level