Working on the Ads Features & Performance team -- a horizontal team across Google's advertisement infrastructure that focuses on advertiser experience and their return on investment.

Improving Time-to-Serve for advertisers, which is the time between Ad creation and the first desired outcome (typically first impression). Involves identification and measurement of key Ad delivery checkpoints, and improving SLA of cross-functional infrastructure.

Improving Budget Utilization by dynamically setting target spend, which improves Advertisers impression of Google ads and improves retention.

Data point-of-contact for Pages Data -- upper funnel of Ads & Business Platform

Create scalable, efficient, and forward-looking datasets used by hundreds of employees as a source of truth to service data needs; from machine learning features to trend analysis

Designing and published "one-pager" dashboards to track metric goals and report on earnings

Data-driven product guidance by assessing opportunity gain of preposed features

As a namespace owner, tasked with resource management across the Pages organization to reduce cost, latency, and redundancy

Bi-weekly presentation to the Ads & Business Platform Org, highlighting success stories to achieving metric goals

Deep-dive dashboard automation API to allow for distinct count data across non-MECE dimensions and auto-publish a dashboard with multi-selectors

Growth Anomaly Detection API to diagnose and alert on company growth metrics

Leading design reviews of data models preposed by colleagues

Interviewing and mentoring new hires

Created end-to-end data unit-testing framework for Facebook search queries

Improving logging coverage by updating client endpoints

Created unit-testing framework and unit-tests for firmware installations on the Windows-OS, retail, light scattering instruments

Created a continuous integration system updating remote instrument firmware, and preforming unit testing

Created a Windows installer (NSIS) for new devices

Web-scraping and formatting of NCBI data-series

Refactored post-doc code to run in on multiple machines in parallel to achieve results by deadline

Performed accuracy tests on three machine learning algorithms using ten-fold, stratified cross-validation across all samples

Developed basic parameter smoothing algorithm that narrowed potential biomarkers of research ailments (ADHD, various cancers)