Billions of users use Google Search Engine daily to find information, and I am very proud of my current work in search engine space. 20% Projects:- Data Science Faculty: teaching Googlers data science fundamentals - Experimentation Lead: building large scale solutions to amplify experimentation at scale- People Pillar Lead: leading conversations and initiatives on outreach and recruitment- IamRemarkable Lead: facilitate and propose self-promotion workshops especially for underrepresented groups

I powered Amazon's worldwide A/B testing platform with analytics and statistical insights to understand customer behavior that enables business stakeholders to make million-dollar, highly critical launch decisions at the click of a button. Projects:- Baseline Imbalance - Population Bias in Weblab (A/B test): September 2016 - Weblab (A/B test) Monetization - ROI Portfolio Analysis: February 2017 - Pre-Trigger Alarm Detection in Weblab (A/B test): March 2017- Pre-Trigger Alarm Mitigation in Weblab (A/B test): Jan 2018- Weblab On-Demand: Created technical framework for new metrics onboarding and method prototyping. Initial product launch (Nov 2017) had 19 metrics from 6 teams, including Amazon Alexa, Video, and Payments. Current queue (Jan 2018) has 21+ metrics from 13 teams awaiting to on-board, including Amazon Music, A9, and Checkout. - Active Weblab (A/B test) Bar Raiser. Training future bar raisers via 1-on-1 and group sessions. - Experimentation for non-prime customers: 2018 - Hybrid (parallel) Experiments: 2019- Holdout Experiment: advanced & long-term experimentation: 2019Presentations:- Wo[men] of CXT - Grace Hopper Edition: November 2016 - Grace Hopper Celebration Takeaways CXT: October 2017- Weblab A/B test Monetization: May 2017- Weblab A/B test Training for Twitch Prime: November 2017- Bar Raiser Training for A/B testing - Consumer Electronics Tech: (multiple sessions) Jan 2018- Statistics 101 for A/B testing: Jan 2018 - Weblab A/B test 101 for A9 (Amazon Search): Jan 2018 - Weblab A/B test On-Demand Metrics: Jan 2018- Statistics 101 for A/B testing - Alexa: Feb 2018 - Weblab Analytics Deep-Dive - Community Shopping: July 2018 - Weblab Analytics for Amazon Echo: September 2018Machine Learning University Courses:- Introduction to Machine Learning at Amazon (IMLA)- Mathematical Fundamentals for Machine Learning (MATH)- Introduction to Data Science (DATA)- Regression for Machine Learning (REGR)

- Data Mart: Engineered base infrastructure to develop Oracle CUBE in ETL to provide multidimensional visibility into Recognized Customer Visits (by Mobile, Traffic Channels, Prime/Non-Prime, Product Category etc.) from clickstream data—led 4 presentations for business and technical community.- Data Modeling: Designed data architecture to capture top 20 High Value Actions (HVA). HVA is any action that maximizes a customer’s lifetime value as defined by DSI (1st Prime/Student/Mom Signup, 1st Amazon Smile Purchase, 1st Mobile App Sign-in etc.). Centralized HVA data repository for all geographies worldwide. - Reporting: Proposed and redefined Investor Relations “active customer” reports. These numbers are shared with external Amazon shareholders at the quarterly earning calls by Jeff Wilke, CEO of Worldwide Consumer. - Identified new KPIs and managed WBR/MBR/QBR and Investor Relation reporting (S-team goal) for 13+ countries to support 1.3 billion annual marketing spend, and led high-priority deep-dives to root cause data quality issues. Presentations: - 2015 Traffic and Consumer Analytics Boot Camp (business audience):Presenter- Data Engineering Meet-up (technical audience): Presenter- Traffic Managers Meet-up Q1-2015 (business audience): Presenter - Traffic Managers Meet-up Q3-2014 (business audience): Presenter

• Utilized Excel, VBA, SQL, Perl, ETL, and other business intelligence systems to analyze traffic metrics and measure channel effectiveness through which customers enter Amazon.com and global websites• Coded a 300+ lines Excel VBA macro to restructure and automate the WBR process, saving 60-90 min/weekly to meet a 7 hour deadline on Mondays• Reduced data delays by 30% and improved data accuracy by optimizing the monitoring and validation processes • Created AmazonSmile WBR deck using Excel and PivotTables to report weekly traffic metrics for management review• Performed data analysis on AmazonSmile customers, providing in-depth visibility into customer purchasing behavior that directed marketing initiatives to improve customer retention—earning 1st place among 40 intern presentations

• Identified KPIs and implemented Google Analytics dashboards, saving the team 104 hours of repetitive work annually • Analyzed company’s web traffic and blocked internal IP addresses tracking to increase reports’ accuracy• Presented a Google Analytics crash course to Utrip executives and created a 5+ page report for future reference