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Sr. Software Engineer Data Scientist

bebo Technologies -
Chandigarh

Apply Now

Job details

Qualifications

NumPy
R
Tableau
SQL
Pandas
AWS
Quality assurance
Redshift
Communication skills
Python

Full job description

bebo Technologies is a leading complete software solution provider. bebo stands for 'be extension be offshore'. We are a business partner of QASource, inc. USA[www.QASource.com]. We offer outstanding services in the areas of software development, sustenance engineering, quality assurance and product support. bebo is dedicated to provide high-caliber offshore software services and solutions.
Our goal is to 'Deliver in time-every time'.
For more details visit our website: www.bebotechnologies.com
Let's have a 360 tour of our bebo premises by clicking on below link:
https://www.youtube.com/watch?v=S1Bgm07dPmMKey

Qualifications Required:

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-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

Apply Now

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