Vardhmaan – Python, Databricks, R

A consumer cybersecurity platform featuring a redesigned user experience and a conversational AI assistant, with a focus on measuring onboarding, engagement, risk resolution, and feature adoption to improve customer experience and inform product decisions.

• Led product analytics and data science for the consumer cybersecurity experience, managing a team of five and collaborating with Product, Engineering, UX, Marketing, and Data Platform teams;
• Defined and implemented a KPI framework covering onboarding, risk resolution, engagement, feature adoption, and conversational AI interactions;
• Partnered with Data Engineering to develop the analytical data model and semantic metrics layer supporting product reporting and in-depth analysis;
• Conducted behavioral and cohort analyses across engagement, notifications, and user journeys, identifying opportunities to improve customer retention and experience;
• Identified repeat-user fatigue following security risk resolution, providing insights that informed product enhancements and re-engagement strategies;
• Led analytics for an AI-powered scam protection suite that reached approximately 2.4 million active enrollments;
• Analyzed onboarding funnels and conducted experiments to identify friction points and improve the unified setup experience;
• Translated analytical findings into actionable recommendations for product improvements and roadmap decisions.

A consumer cybersecurity growth optimization initiative focused on improving onboarding, feature adoption, pricing, and customer acquisition through product analytics and experimentation, contributing to higher installation completion, feature enrollment, and new-customer bookings.

• Led analytics for the unified onboarding experience, using funnel analysis and experimentation to identify friction points and inform product improvements that increased installation completion from 60% to 65% and adoption of two or more protection features from 26% to 29%;
• Designed and analyzed 13 A/B tests across Android and iOS to optimize pricing and customer acquisition, contributing to a broader growth program associated with approximately 46% YoY growth in new-customer bookings;
• Evaluated a desktop-to-web cross-authentication experience that reduced sign-in friction and contributed to approximately 10% higher feature enrollment;
• Standardized experimentation practices across product teams, including hypothesis formulation, primary and guardrail metrics, sample-size estimation, and results interpretation;
• Supported a program of over 100 experiments and developed reusable experimentation tools, including a Databricks sample-size calculator and Python modules for A/B testing;
• Conducted retention and uninstall analyses using product telemetry, onboarding behavior, device attributes, acquisition sources, and subscription data;
• Applied machine learning and SHAP explainability techniques to identify drivers of customer behavior and translate findings into actionable product hypotheses.

An advertising revenue analytics initiative focused on automating monetization reporting, improving campaign performance, and providing data-driven insights to support sales, account management, and business growth across global markets.

• Built advertising revenue reporting infrastructure from scratch using SQL, developing automated BI dashboards for sales, account management, and client solutions teams;
• Automated recurring revenue analytics and reporting workflows, eliminating approximately 20 hours of manual work per week;
• Conducted campaign performance and monetization analyses, identifying optimization opportunities that supported an approximately 5% improvement in cost per click (CPC);
• Applied advanced analytics and data mining techniques to identify revenue growth opportunities and inform product and advertising optimization strategies;
• Collaborated with Product Management, Sales, Account Management, and Engineering teams to translate business requirements into analytical solutions and actionable insights;
• Helped establish regional monetization analytics capabilities as the first analytics hire outside mainland China;
• Built and developed an analytics team of 10 analysts supporting global markets, directly managing three team members and providing guidance to the broader team.

A digital advertising analytics initiative focused on improving revenue growth, advertiser retention, product adoption, and go-to-market strategies for mid-tier advertisers through predictive modeling, customer segmentation, and data-driven decision-making.

• Developed analytics and reporting capabilities for Google Ads, supporting advertiser performance, revenue growth, retention, and product adoption;
• Built a regression-based customer churn model to identify at-risk advertisers and inform retention strategies;
• Developed a lead scoring model to prioritize high-value advertising prospects and support sales and acquisition efforts;
• Built a customer engagement model to identify high-value product features and analyze their relationship with user adoption and engagement;
• Conducted product analytics and data mining to identify key drivers of customer engagement and opportunities for product improvement;
• Defined and monitored key performance indicators for advertiser revenue, campaign performance, product adoption, and go-to-market initiatives;
• Analyzed internal and external market data to identify growth opportunities and support go-to-market strategies for new advertising products;
• Combined quantitative and qualitative research to understand the advertising ecosystem, inform program planning, and support data-driven business decisions.