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

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.

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

Applied Data Scientist, Operations Analytics

Key skills