About
Data Analyst with hands-on experience translating raw operational and business data into measurable outcomes using Python, SQL, and Power BI. Currently automating data-driven workflows at TCS; prior project work spans customer segmentation, revenue forecasting, and predictive modeling across retail, finance, and healthcare domains. Comfortable owning a dataset end-to-end: extraction, cleaning, modeling, and stakeholder-ready reporting.
Skills & Expertise (27)
Work Experience
Engineer – Operations Automation Support
Tata Consultancy Services
Aug 2025 - Present
Extract, clean, and analyze operational data from ServiceNow to identify recurring incident patterns and process bottlenecks, feeding findings into monitoring and alerting improvements. Build Python-based reporting and automation workflows that consolidate operations data into structured summaries, reducing manual data compilation effort for the team. Design monitoring/alerting logic informed by historical ticket data trends, improving early detection of recurring issues. Partner with cross-functional stakeholders to translate raw system data into process-improvement recommendations.
Data Analyst
Retail & Marketing Analytics
Present - Present
Segmented 10,000+ customers via RFM analysis and K-Means clustering in Python; identified the top 20% of customers driving 65% of revenue. Built Power BI dashboards tracking revenue, CLV, and retention trends, cutting manual reporting time by ~30% (10 hrs/week). Translated segmentation insights into targeting recommendations for the marketing team, supporting more focused campaign spend.
Data Analyst
Financial Operations Analytics
Present - Present
Built a Python/Pandas/Prophet forecasting pipeline projecting 12-month revenue trends to support budget planning. Developed a churn classification model (~75% accuracy) to flag key attrition drivers ahead of renewal cycles. Automated the ETL workflow with Airflow, cutting manual data-prep effort by ~12 hours/week.
Data Analyst
Healthcare Analytics
Present - Present
Cleaned and engineered features from a 130-hospital patient dataset; benchmarked Logistic Regression and Random Forest models via grid search. Built dashboards summarizing readmission risk factors to support faster clinical resource-allocation decisions. Framed model limitations and next steps for stakeholders rather than overstating a single accuracy metric — readmission prediction is a known hard problem in this dataset.
Education
B.Tech in Computer Engineering - DY Patil's Ramrao Adik Institute of Technology
2021 - 2025 · Afghanistan
Certifications
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Skills (27)
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