Required Skills :

- 9+ years of experience in a data scientist, ML engineer, or advanced analytics role.

- Strong foundation in statistics - hypothesis testing, regression, time series analysis, Bayesian methods.

- Advanced SQL - comfortable writing complex queries across large, multi-source datasets.

- 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.