Sharan Venkatesh

Data Scientist

Boston, Massachusetts, United States11 mos experience

Key Highlights

Achieved 18% customer reactivation rate through targeted campaigns.
Reduced churn by 10% with predictive analytics and segmentation.
Boosted holiday revenue by 25% using A/B testing strategies.

Stackforce AI infers this person is a Data Analyst with expertise in telecommunications analytics and predictive modeling.

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Skills

Core Skills

Data AnalysisPredictive AnalyticsForecasting

Other Skills

A/B TestingCustomer SegmentationSQLPythonChurn PredictionDemand ForecastingReportingGoogle AnalyticsSnowflakeTableauMicrosoft ExcelStatisticsR (Programming Language)Full-Stack DevelopmentSoftware Development

About

Master's candidate in Analytics (Applied Machine Intelligence) at Northeastern University (GPA: 3.9), with nearly 2 years of hands-on experience as a Data Analyst. In my previous role, I led client analytics initiatives, reducing churn by 10%, driving an 18% customer reactivation rate, and boosting holiday revenue by 25% through A/B experimentation and predictive modeling. At Northeastern, I've extended that work into advanced territory: building an LLM-powered RAG platform, applying statistical modeling to 1.3M+ real-world service requests, and developing deep learning forecasting models. I specialize in the full analytics workflow: SQL and Python for data wrangling, Tableau and Power BI for executive-facing dashboards, and cloud platforms like Snowflake and Databricks for scalable data solutions, always focused on translating numbers into action. Open to Data Analyst, Business Analyst, Analytics Engineer, and BI Analyst roles where I can bring both technical depth and business impact.

Experience

11 mos

Total Experience

Average Tenure

Current Experience

Sarada technologies

2 roles

Data Analyst

Jun 2023 – May 2024 · 11 mos · Chennai · On-site

Achieved an 18% reactivation rate by segmenting 100K+ inactive prepaid users for a regional telecom client by analyzing recharge frequency and engagement trends, then launching a personalized win-back campaign that drove a 20% month-over-month increase in new activations.
Reduced churn rate by 10% and improved customer satisfaction by 4% by employing predictive analytics and customer segmentation to design a new loyalty program, identifying high-churn customers and launching a retention-focused rewards program.
Boosted holiday revenue from new-customer conversions by 25% by driving strategic A/B experimentation and establishing a framework that guided campaign design and targeting.

Data AnalysisPredictive AnalyticsA/B TestingCustomer SegmentationSQLPython

Data Analyst Intern

Oct 2022 – May 2023 · 7 mos · Chennai · On-site

Improved churn prediction accuracy by 25% for a telecom client by identifying key indicators (recharge frequency, usage trends, call activity) and building a forecasting model, enabling proactive retention outreach.
Enhanced SIM inventory accuracy by 20% by building a demand forecasting model using historical activation patterns, reducing stockouts during peak enrollment periods.
Implemented standardized design patterns and reporting templates across Customer Lifecycle and Growth Analytics teams, improving dashboard reusability and readability while reducing report development time by 20%.
Enabled data-driven executive decisions by building and presenting Weekly and Monthly Business Review reports that tracked activation, retention, and revenue KPIs across customer segments.

Churn PredictionDemand ForecastingReportingSQLPythonData Analysis+1