Senior Software Engineer_Data Scientist

bebo Technologies; Chandigarh; On-site; INR 900,000 - 1,500,000; Full time

Job summary

bebo Technologies is seeking a seasoned data scientist/ML engineer to shape our analytics and forecasting capabilities for a complex digital ordering pipeline. You will build and own metrics, dashboards, and predictive models that drive business decisions and improve system reliability.

Ideal candidates combine strong statistical foundations with practical production experience in data platforms and BI tooling, and can communicate insights to executives and engineers alike.

Qualifications

• Strong foundation in statistics including hypothesis testing, regression, time series analysis, Bayesian methods.
• Advanced SQL with complex queries across large, multi-sourced datasets.
• Proficiency in Python or R for analysis, modeling and automation.
• Experience with ML/statistical libraries such as scikit-learn, statsmodels, pandas, NumPy.
• Experience with AWS data and ML services (SageMaker, Redshift, Athena, Glue, QuickSight).
• Hands-on experience with Tableau for dashboards.
• Ability to define metrics frameworks and build dashboards from scratch.
• Experience building anomaly detection or predictive models in production or operational contexts.
• Excellent communication to present statistical findings to executives, engineering leaders and technical teams.
• Experience collaborating across multiple teams or systems to synthesize data into a unified view.

Responsibilities

• Define and build the metrics framework for the digital ordering pipeline from intake to delivery.
• Design and deliver dashboards tracking order volume, throughput, turnaround times, error rates, and system stability across integrations.
• Build predictive models to forecast order failures, volume trends, and capacity needs.
• Develop automated anomaly detection to surface pipeline issues before escalation.
• Apply statistical methods for root cause analysis and diagnosing why systems fail.
• Partner with engineering teams to instrument data collection where gaps exist.
• Translate complex technical findings into clear narratives for leadership and teams.
• Investigate ad-hoc data questions and diagnose production issues and incidents.

Skills

Statistics

SQL

Python/R

ML libraries

AWS SageMaker

Tableau

Metrics & dashboards

Anomaly detection

Cross-team collaboration

Communication

Tools

SageMaker

Redshift

Athena

Glue

QuickSight

Job description

Requirements

• 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 escalapop
• 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