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
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8+ years of experience in a data scientist, ML engineer, or advanced analytics role
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Strong foundation in statistics — hypothesis testing, regression, time series analysis,Bayesian methods
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Advanced SQL — comfortable writing complex queries across large, multi-sourcedatasets
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Proficiency in Python or R for analysis, modeling, and automation
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Experience with ML/statistical libraries (scikit-learn, statsmodels, pandas, NumPy, orsimilar)
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Experience with AWS data and ML services (SageMaker, Redshift, Athena, Glue,QuickSight, or similar
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Hands-on experience with Tableau
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Demonstrated ability to define metrics frameworks and build dashboards from scratch,not just maintain existing ones
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Experience building anomaly detection or predictive models in a production oroperational context
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trong communication skills — able to present statistical findings to executives,engineering leaders, and technical teams with equal clarity
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Experience working across multiple teams or systems, synthesizing data from disparatesources into a unified view
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Define and build the metrics framework for the digital ordering pipeline — from order intake through result delivery
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Design and deliver dashboards that track order volume, throughput, turnaround times, error rates, and system stability across multiple integration points
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Build predictive models to forecast order failures, volume trends, and capacity needs
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Develop automated anomaly detection to surface pipeline issues before they escalate
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Apply statistical methods for root cause analysis — diagnosing why systems fail, not just what failed
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Partner with engineering teams to instrument data collection where gaps exist
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Translate complex technical and statistical findings into clear narratives for executive leadership, engineering management, and individual engineering teams
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Investigate ad-hoc data questions — diagnosing production issues, quantifying impact of incidents, and supporting root cause analysis
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Document metric definitions, model logic, data sources, and dashboard design so the organization can maintain and extend your work independently
