Pramath

Data Analyst with 3+ years in SQL, Python, funnel analysis, cohort analysis, and A/B testing across fintech payments and product analytics. Reduced CAC by 18%, improved conversion by 14%, and achieved 86% pipeline extraction accuracy through experimentation, churn modeling, and Snowflake-based workflows. Builds automated dashboards, ETL pipelines, and behavioral segmentation models delivering actionable insights to cross-functional stakeholders.

MS in Data Analytics Engineering with expertise spanning transaction analytics, marketplace growth, and customer behavioral analytics.

Data Analyst at Toast (2026-02 – Present)

Customer analytics and business intelligence for Toast's restaurant platform, supporting product and operations teams across 164,000+ locations.

• Built churn prediction model in Python (XGBoost) on 14,640+ records, achieving 0.96 recall to surface retention and LTV risk patterns across customer cohorts.
• Surfaced 89 behavioral segments from 8,500+ customer records via unsupervised clustering, uncovering complaint themes absent from labeled categories.
• Executed A/B testing protocols with SHAP interpretability to measure product metrics across activation and engagement funnels, delivering behavioral analytics findings to product and operations teams weekly.
• Analyzed funnel drop-off patterns using SQL and Python across 8,500+ interaction records, translating findings into stakeholder recommendations.
• Automated reporting in Hex consolidating sentiment trends and churn risk scores, cutting manual prep across 3 stakeholder teams.

Data Analytics Capstone at George Mason University (2025-08 – 2025-12)

Production-grade analytics pipeline built on federal contract data, applying Python, SQL, and Snowflake to enable evidence-based insights at scale.

• Built production-grade analytics pipeline on Snowflake ingesting 1,400+ federal contract PDFs using Python, OCR, and Document AI, achieving 86% extraction accuracy and reducing manual review by 70%.
• Developed SQL-based analytical models to surface contract-level anomalies and data quality gaps, enabling downstream experimentation and evidence-based insights for governance stakeholders.
• Designed scalable data ingestion, validation, and monitoring workflows ensuring continuous pipeline reliability across a large-scale, multi-format document corpus.

Data Analyst at Uber India (2022-07 – 2024-01)

Marketplace analytics for Uber India's City Operations and Growth teams across 100+ cities.

• Reduced CAC by 18% across 3 metro markets using SQL (Presto) and Python for rider cohort segmentation and campaign targeting.
• Improved trip conversion by 14% through A/B testing of geo-targeted promotions using Python and Tableau.
• Automated ETL pipelines in Airflow and BigQuery, cutting reporting effort by 30% for City Operations stakeholders.
• Analyzed funnel drop-off patterns on 50,000+ monthly rider records using SQL, surfacing insights for product and growth teams.
• Maintained weekly business intelligence dashboards in Tableau tracking DAU, surge frequency, and promotional efficiency across 4 city teams.

Data Analyst at Razorpay (2021-01 – 2022-07)

Transaction data analytics and merchant performance reporting for Razorpay's Risk and Operations teams.

• Queried transaction datasets using SQL to track payment gateway performance and merchant KPIs, supporting daily reporting for Sales and Operations teams.
• Maintained Power BI dashboards monitoring GTV trends and MoM variance across 40+ merchant accounts, improving visibility into high-value merchant performance.
• Automated 60%+ of recurring MIS reports using Python and Excel, reducing manual reporting effort for Risk and Operations stakeholders.
• Identified fraud detection signals and failed payment patterns across merchant segments using SQL and Metabase, surfacing root causes to Risk and Operations teams.

Master of Science in Data Analytics Engineering – George Mason University (2025-12)

Bachelor of Technology in Computer Science – PES University (2022-07)