Aug 2017 – Jun 2022 · 4 yrs 10 mos · New York, New York, United States

• I was the tech lead across 4 teams (9 engineers) to build Amazon Advertising's ability to produce online measurement of machine learned models for completely unrecognized traffic. I architected the solution which allows teams to see the actual impact their models would have on traffic. Presented the solution to Amazon's engineering leadership via a formal org-wide design review. (ML, EMR, Spark, Java, Python)
• I was the tech lead across 6 teams (7 engineers) to allow frequency capping on unrecognized traffic via machine learned models. I architected the solution which allows Amazon to better service Advertisers and viewers as customer identities are becoming more anonymized. Presented the solution to Amazon's engineering leadership via a formal org-wide design review. (ML, EMR, Spark, Lambda, Java, Python, Kinesis)
• I was the tech lead across 2 teams (4 engineers) to start surfacing frequency cap blocklists from my team to the Amazon Ad Exchange. I architected a scalable solution to handle ~100k reads per second and ~80k writes per second. (Redis, Lambda, Java, Python, Kinesis)
• I was a founding member of the modeled frequency capping team. I have built a culture of technical/personal growth, individual ownership, and team collaboration. This was via tech demos, book clubs, shared cooking recipes, and joint working sessions. We have gone from only 2 engineers to now 10 engineers, 2 applied scientists, and 3 applied scientists on loan.
• I was the tech lead for my team (4 engineers) to build the Amazon Advertising forecasting flow with a publisher-subscriber model. This decreased my team's latencies by 50%, gave our data science team all of the campaign data, and provided full ownership over the forecasting flow. I designed how the new systems will interact, implemented a new service, and mentored the team of engineers throughout the entire project. We launched successfully on time. (REST, Postgres, Kinesis, Java)

May 2016 – Aug 2016 · 3 mos

• Worked on the Advertising department under Inventory Forecasting Systems.
• Flew to Seattle with my manager and mentor to present my project to the Placement Engineering Team at Amazon. The project would help inform their decisions and health analysis using our big data and custom visualization techniques. Built Machine Learning models to help predict revenue to further drive insights to the teams affected.
• The trip to Seattle helped me understand how to market new platforms to a wide variety of audiences. Also was taught how a proof of concept helps a lot more tremendously to start collaboration, as opposed to a full-fledged platform that could have pointless features.

May 2015 – May 2016 · 1 yr · UC Berkeley

• I connect University Recruiters with UC Berkeley Computer Science students for full-time and internship positions through the means of info sessions, tech talks, etc.
• Have partnered with Amazon, Microsoft, Arista Networks, and more!

May 2015 – Aug 2015 · 3 mos · Foster City, California

• Worked on Token Lifecycles which allows banks to have the ability to securely disable an ewallet (Apple Pay, Google Wallet, etc.) payment.
• Learned how to architect my own software project, layout a database, and how to use the Spring framework to succeed in my final project.