Jul 2022 – Mar 2024 · 1 yr 8 mos · San Francisco Bay Area

• I lead the Marketplace Machine Learning and Artificial Intelligence function over DoorDash's Core App. Some problems we solve are include Search Ranking / Retrieval, Personalization, Pricing and Promotions Optimization, User Profile and Targeting and leveraging LLMs.

Jun 2020 – Aug 2022 · 2 yrs 2 mos

• I lead Overstock's Algorithms organization that consists of Search Sciences (Machine Learning Ranking, Core Search, UI), Sponsored Products (Auction Dynamics, Production Systems, Partner Interface), Core Machine Learning (Pricing Machine Learning, Delivery Forecasting, Customer Segmentation) and Recommendations Systems. We focus on empowering the business with innovative algorithmic products using state of art machine learning practices and handle all aspects of the product including R&D, productization, user experience and the backend systems that support it. My main area of focus is organic and sponsored search ranking, auction dynamics, recommender systems and personalization and core machine learning optimizations at production scale.

Nov 2019 – Jul 2020 · 8 mos

• I head the Search Sciences Org under the Chief Algorithms Officer. The Org comprises of Search Ranking (Machine Learning), Search Systems, Search UI & Search Operations. Collectively, we own the search experience on the site including the relevance (SOLR & LTR based reranking methods), autocomplete, Quickview, featured products, and promotional page experiences, and auction systems for Overstock Sponsored Products.

Jan 2019 – Dec 2019 · 11 mos

• The aim of this initiative is to reduce the friction from our customers' search and discovery experience. This is currently the biggest initiative for the company in terms of monetary value and resource commitment, and we have made numerous enhancements to the site that lead to hundreds of millions in increased sales. The initiative covers work from the User Experience, Machine Learning, Algorithms, Taxonomy and Attributes, and Infrastructure teams. My role is mostly making sure the work is towards a consistent vision and that we are approaching the problems effectively. I get to learn about and shape a lot of different aspects of Search. We were the largest initiative this year, and delivered >20% over target GMS.

Sep 2018 – Nov 2019 · 1 yr 2 mos

• I founded and lead the Ranking Squad at Overstock. We apply Machine Learning practices to improve all of Overstock Search. In addition, we are building, and own the Sponsored Search product. We've built state of the art Real-Time machine learning systems to support this goal. The Ranking Squad comprises of an amazing group of ML Scientists, Engineers, Analysts and DevOps.

Jan 2018 – Feb 2019 · 1 yr 1 mo · Greater New York City Area

• I was a founding member of the company, built the initial product from scratch, and led a team of ~7 engineers to take the product through multiple iterations as we discovered our business value. Took the product to TechStars Accelerator, and Nami (CEO) has since raised $1.2M in seed funding. Currently just acting as a technical advisor to the team that is hands on.

Feb 2014 – Sep 2018 · 4 yrs 7 mos · Brooklyn, NY

• I worked on surfacing the most relevant products to users using machine learning techniques and experimental methods. These led to hundreds of millions of dollars in increased GMS for the company. I've built infrastructure and tooling for featurization, inference and metrics pipelines to support this work. Previous to this role, worked on scalability and deployment problems as part of the Infrastructure org.

Jan 2012 – Jan 2014 · 2 yrs · Boston, MA

• I mostly worked on Infrastructure. The most notable experience for me was building a packet capture and network traffic monitoring tool from scratch (Wiff) that is got open sourced.

May 2011 – Aug 2011 · 3 mos · Greater Boston Area

Aug 2010 – Aug 2011 · 1 yr

• Help students machine their design products, design parts for the shop as required, and improve the efficiency of the shop. Currently working on instruction videos to acquaint students to shop and machining techniques.