Customer Churn Analysis
Analyzed 70K+ telecom records and built ML models (~88% accuracy) to uncover churn drivers and support retention.
Data Analytics & Operations
I work at the intersection of data analytics and operations — turning messy datasets, recurring workflows, and business questions into clear reports, dashboards, and decisions.
Detail-oriented analyst focused on data cleaning, performance reporting, and operations that help teams decide with less manual effort.
I enjoy building practical analytics systems — clean datasets, reliable SQL, validation scripts, KPI dashboards, and reporting workflows that turn data into something a team can actually act on.
Cleaned large datasets (50K–200K+ records), optimized SQL queries, built Power BI dashboards for SLA and delivery metrics, automated recurring reports, and wrote Python validation scripts to improve data quality.
Supported operational and customer-facing workflows with a focus on accuracy, reporting, and execution.
Analyzed 70K+ telecom records and built ML models (~88% accuracy) to uncover churn drivers and support retention.
A 5-page Power BI dashboard connected to MySQL with DAX metrics for month-over-month and year-over-year analysis.
Processed 3M+ public health records into interactive state-wise views of infections, vaccinations, and mortality trends.
A multi-view Tableau dashboard highlighting attrition risk by department, tenure, and compensation.
Python, SQL, Power BI, Excel, Tableau, Looker Studio, BigQuery, and Git.
Data cleaning, EDA, statistical analysis, data modeling, and A/B testing.
KPI reporting, SLA tracking, workflow automation, and stakeholder-ready dashboards.
Open to thoughtful analytics, reporting, and operations-focused work.