Resume: Aarya Bhivsanee

Product & Data Analytics Intern

• Defining the retention measurement framework (activation, active, retained, churned) for a two-sided credit exchange and mapping the 5 priority member journeys (onboarding, invites, send credit, ordering) to find drop-off before a first trade.
• Designing an event tracking plan to capture the abandoned attempts the database misses, tagging each drop-off as UX friction or market (supply) friction, with a ranked friction audit to prioritize UI/UX fixes alongside A/B test data.
• Building Python visualizations and reports on transaction, credit balance, treasury wallet and retention data while migrating the member dashboard to the new platform, QA testing features and supporting product scoping and documentation.

Data Science Intern

• Cleaned and engineered features across a 768-patient, 8-variable clinical dataset (glucose, BMI, insulin), preparing the data for predictor and risk analysis.
• Identified glucose, BMI, and age as the strongest predictors of diabetes risk through variable contribution analysis, translating findings into stakeholder-ready visualizations of risk-factor and segment-level patterns.

SaaS Retention, Churn and Early-Warning Analytics

• Flagged accounts at elevated churn risk: HIGH and MEDIUM-risk accounts churned at about 17% a month versus 7% for LOW across 24 months, using a rule-based score with reason codes (usage drop, errors, low CSAT, slow resolution)
• Showed where customers are lost: retention fell from 90.1% at month 1 to 57.4% at month 6 using PostgreSQL cohort curves
• Found churn worsening: monthly logo churn rose from about 6% (May to Jul 2024) to 8 to 11% (Aug to Dec), lifting churned MRR to $400K to $500K a month; 1–5 seat accounts lost about 18% of MRR a month

E-commerce Funnel Analysis

• Located where the funnel leaks: 97.8% of viewing sessions never reach cart, while 72.8% cart abandonment sits near the roughly 70% industry average, pointing optimization to the product page and add-to-cart step
• Found returning visitors converting at about 3x new visitors from 3 to 9 pm and several high-traffic categories below average, shaping 3 proposed A/B tests (product page, category UX, cart recovery)
• Built a PostgreSQL session mart (1,761,675 sessions; 30-minute sessionization, window functions) feeding 4 Tableau dashboards on funnel health, segmentation, category performance and time to convert

GMV Anomaly Detection & Revenue Driver Attribution

• Built an anomaly detector using MAD-based robust z-scores on detrended GMV residuals, surfacing 12 revenue anomalies across 18 months with severity tiers; validated by catching all 3 synthetic shocks and flagging Black Friday as a +558% spike (z=29.79)
• Attributed each anomaly to its revenue drivers (Black Friday: 72% of the delta from Southeast Brazil) and automated explanations: an LLM limited to precomputed fact packets writes What/Why/Action briefs where every number traces to the data

Master of Science in Data Science

Coursework: Statistical Methods, Marketing Analytics, Applied Machine Learning, Web Mining

Bachelor of Engineering in Artificial Intelligence and Data Science