Data Scientist

Key role and 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.

- Investigate ad-hoc data questions diagnosing production issues, quantifying impact of incidents, and supporting root cause analysis.

- Document metric definitions, model logic, data sources, and dashboard design so the organization can maintain and extend your work independently.

Must have :

- 8+ 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.

- Strong communication skills able to present statistical findings to executives, engineering leaders, and technical teams with equal clarity.

- Experience working across multiple teams or systems, synthesizing data from disparate sources into a unified view.

Good to have :

- Familiarity with healthcare, diagnostics, or lab operations.

- Experience with operational analytics (error tracking, SLA monitoring, system health metrics).

- Experience with real-time or streaming analytics (Kinesis, Lambda).