Led the design and implementation of a highly available and scalable automatic report management and generation server, unifying all major company reporting requirements under a single, efficient processing system

Architected, designed, and implemented a universal event-based alert system, integrating it with the attribution report server to streamline debugging and inter-team collaboration and ensure client SLA compliance

Designed and developed an automated attribution reporting system with Python and Spark to analyze LG TV audience reactions and conversions from ad exposures (including app launch and installation, audience engagement and acquisition metrics), delivering comprehensive reports to LG Ad Solutions USA

Designed and built a UI dashboard and its web server with Python, Javascript, React, and Flask, streamlining attribution report request ingestion, validation, and result visualization for internal and external stakeholders

Developed a scalable attribution reporting engine using Spark and Scala to measure the closed-loop performance of TV/OTT/Digital ad campaigns, driving web, mobile, and foot traffic

Researched and developed strategies to assess ad effectiveness, including the impact from network, daypart, recency, and frequency, test-control group selection and lift analysis, and location visits data refinement, using cross-domain data modeling of TV viewing behavior and demographic attributes from 34 million U.S. households

Developed data pipelines using Airflow for viewership and 3rd party web/mobile/location data ingestion and transformation, powering downstream services and improving analytics team efficiency by 20%

CA for EECS E6720 Bayesian Models for Machine Learning (Fall 2017)

Developed a web crawler with Python and Kafka to fetch viewers’ activities from Twitter, Facebook, and Instagram

Implemented a lexicon-based approach to perform sentiment analysis on 300K tweets across 2 months