Sr. Software Engineer_Data Scientist

Qualifications Required:

• 6+ 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-sourcedatasets
• Proficiency in Python or R for analysis, modeling, and automation
• Experience with ML/statistical libraries (scikit-learn, statsmodels, pandas, NumPy, orsimilar)
• 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 oroperational context
• trong 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 disparatesources into a unified view What You'll Do:
• 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