Engineered a testing framework to analyze Mercari item search results merged with results from Elastic's sparse encoder, Elser. The test used the nDCG as implemented in sci-kit-learn, and real-world e ngagement data for validation. Created and deployed RESTful services on Google Cloud Platform to tailor recommendations for brands, and categories, enhancing user experience. Led training programs for interns and new employees, focusing on rapid upskilling and integration into project teams. • Implemented a query expansion system, employing gensim for embeddings, Milvus for search indexing, and Python with Polyaxon for orchestration, focusing on precise user segmentation. • Built an anomaly detection service based on Fbprophet for predictive analytics and Slack for real-time alerts.built test system for query expansion using engagement as groundtruth in nDCG; for user segmentation: embeddings from gensim, search index from Milvus, orchestration via python and polyaxon • Wrote RESTful services for personalized recommendations for brands, categories, and searches, and deployed to GCP • Developed and productionized time series forecasting models to automatically alert when various business measures go out of predicted bounds using fbprophet, altair, and slack for alerting • Generated search similarities using word embedding; and user profiles in the same space for search recommendations • Built data pipeline to generate user preferences for brands and categories from log data; wrote collaborative filtering models using ALS in Implicit; generated NMSLib indexes for searching; deployed for production servicesMentored and trained interns and new hires

Architected and built a distributed, blockchain based search engine by architecting and managing the development and coding of a blockchain-enabled, distributed search engine leveraging Yacy and Ethereum. Wrote code for managing concurrency, peer discovery, updates, and queuing functions within the peer-to-peer network. Designed production-level Python models for document classification, facilitating real-time querying via a RESTful interface, utilizing Flask, Gunicorn, and Docker for deployment. • Transitioned project’s build infrastructure from Maven to Gradle, streamlining build processes and improving project scalability. • Optimized build speeds in China by identifying and integrating suitable Maven Central mirrors.

Developed advanced time series analysis models using ARIMA and later LSTMs in Python for dynamic bid adjustment, which led to a 10% increase in bidding success rate. Responsibilites also include bug fixes and minor features, for the main Ruby codebase, supporting overall system integrity and functionality. Automatically detected anomalies and sent out alerts in Slack when they happened.

Spearheaded the development and ongoing enhancement of a natural language search platform tailored for the real estate sector. This project was based first on Lucene, and later Solr, along with with JTS for geoprocessing. Another important project involved the creation of an autosuggest feature employing Finite State Transducers and market data analytics, which effectively predicts user queries, streamlining the search process and enhancing user engagement.I also implemented targeted advertising within search results and These features allow for detailed location-based filtering, including school districts, neighborhoods, and subdivisions, ensuring users can find properties through proximity and polygon presence. My leadership in developing and executing these search and segmentation features has significantly enhanced user experience and improved data accessibility, setting new standards in the real estate technology space.

Leader in a customer acquisition project involved analyzing third-party data to carve out new customer segments and boost marketing strategies, demonstrating my ability to leverage big datafor substantial business impacts.My technical contributions include enhancing recommendation systems with data processed through Hadoop and being the author of an open-source LSH algorithm. I've engineered a sophisticated collaborative filtering ad targeting system for PayPal on eBay's Hadoop clusters, handling user and merchant data across a broad network of nodes. Additionally, I deployed matrix factorization techniques using alternating least squares in C, optimized with graphchi for enhanced performance, and developed a targeted advertising backend, enabling finely-tuned campaign customization

Where built a location aware advertising platform for smartphone apps, as well as a smartphone app for android and iPhone. Where was acquired by eBay/Paypal in May of 2011.• Wrote search pipeline feeding into recommender system using Hadoop for data processing/transformation and Lucene as the basic search engine.• Built kd-tree based lookup system for finding nearby places• Designed and implemented hadoop/mahout-based item similarity recommendation system using user/location preferences as expressed by ratings.• Incorporated this recommender into a lucene based search engine, with location as part of the preference calculation• Engineered system based on LSH for deduplicating place data.

Wrote a dynamic index switching and updating system that we used until we transitioned from Lucene to Solr. Authored an advanced entity recognition system for company names, neighborhoods, and other geographic entities, streamlining data processing and improving search precision. My expertise extends to optimizing search performance through rigorous iterations using tools like JProfile, Dynatrace, and detailed log analysis, which have substantially improved system efficiency and reliability.

• Wrote most of the personalized websearch product. This project involved doing searches, spidering web pages, scoring them wrt a set of attributes, and using these scores to score against a user profile, and finally presenting the pages in an order determined by this score.• Wrote the threadpool and basic synchronization code used throughout the codebase• Designed and built the production attributization engine (which generated sets of attributes for web pages) and the profile-based recommender of the websearch product.• Wrote a tokenizer and clustering engine for the sponsored links product.

• Algorithm development and implementation for SmartSurf, which used collaborative filtering to suggest pay-per-view titles to cable TV users.• wrote, maintained and extended software that served targeted advertisements to client software on set-top boxes. • Wrote significant portions of software that served targeted advertisements to users of the Predictive client. • Wrote much of the Predictive Client, used by the AT&T WorldNet service to serve personalized ads to internet users. The client was written in C++. It logged web traffic, communicated summaries of this traffic to a server, and displayed targeted advertisements sent down by the server.• Wrote auto-update module for this client, which successfully upgraded 3.5 million AT&T customers

• Wrote a neural network based system to predict credit card fraud• Wrote a module for weather tracking• Wrote code for caching of airport, flight, and hotel information• Wrote code for email notification of ticket purchases, registrations, and session tracking• Wrote code for finding and storing data for mileage accounts, traffic logging• Wrote several spiders/scrapers