Proficiency in Python or R for analysis, modeling, and automation
Experience with ML/statistical libraries (scikit-learn, statsmodels, pandas, NumPy, or similar)
Experience with AWS data and ML services (SageMaker, Redshift, Athena, Glue,QuickSight, or similar)
Hands-on experience with Tableau
Demonstrated ability to define metrics frameworks and build dashboards from scratch, not just maintain existing ones
Experience building anomaly detection or predictive models in a production or operational context
Key Responsibilities:
Define and build the metrics framework for the digital ordering pipeline — from order intake through result delivery
Design and deliver dashboards that track order volume, throughput, turnaround times, error rates, and system stability across multiple integration points
Build predictive models to forecast order failures, volume trends, and capacity needs
Develop automated anomaly detection to surface pipeline issues before they escalate
Apply statistical methods for root cause analysis — diagnosing why systems fail, not just what failed
Partner with engineering teams to instrument data collection where gaps exist
Translate complex technical and statistical findings into clear narratives for executive leadership, engineering management, and individual engineering teams
