Zuhayer Quazi

Backend/API Engineer @ Stripe · (510) 371-2063 · zuhayer@stripe.com

Hi there! My name is Zuhayer and I'm a software engineer at Stripe. I'm located in New York City and am always looking to connect and expand my network! I love watching soccer, going thrifting/shopping, and trying different types of coffee and beers.


Experience

Backend/API Engineer

- Backend engineer on the Self-Serve Compliance team, under Onboarding.

July 2022 - Present

Senior Software Engineer

- Collaborated with product/solutions teams to design and migrate toward common schemas and pipeline processes to replace isolated client implementations, unifying the product to scale to more clients.

- Designed and implemented offline pipeline changes to serve data for the first Medicaid program launch at Nuna (~900k patients), powering the same capabilities for Commercial programs.

November 2021 - May 2022

Software Engineer II

- Designed and built a system for users to update physician eligibility in-app instead of by monthly file submission, the first system at Nuna to process user submitted changes. Eliminated manual processing and saved hours a month by connecting these UI changes to offline data processes.

- Automated and connected critical client data processes using Prefect. Learned and worked with Terraform to handle PHI/authentication permissions

- Mentored an intern on designing a data structure and system that can compute dozens of “Makary Measures” efficiently, which are metrics for identifying overtreatment/low value care.

May 2021 - November 2021

Software Engineer I

- Built configurable pipelines and implemented business logic that allows multiple clients to attribution patients to physicians; impacting an estimated ~16 million patients across the country.

- Collaborated with data analysts to implement data enrichments crucial to value based care.

June 2020 - May 2021

Software Engineering Intern

- Implemented a ‘sunset policy’ data flow to unsubscribe with historically non-engaged users

- Unsubscribed millions of non-engaged/spam/malicious email addresses and increased engagement rate, revenue, and reputation with email service providers

May 2019 - August 2019

Teacher's Assistant

I tought Python and computer science fundamentals to CMU students that have little to no background in programming. Starting from the basic principles of computer science, I guide students in designing, developing, and publishing efficient algorithms through interactive recitations and office hours.

August 2018 - December 2019

Cloud Foundation Engineering Intern

Developed ‘Lifecycle Management as a Service’ (LaaS), a tool that automates patching/upgrading of VMware products and services. Utilized Python/Flask backend with an Angular frontend.

May 2018 - August 2018

Education

Carnegie Mellon University

Bachelor of Science
Statistics & Machine Learning
Graduation:

May 2020

Courses
Introduction to Machine Learning, 10-315
Data Mining 36-462
Advanced Methods for Data Analysis 36-402

Computer Systems, 15-213
Intro to Functional Programming, 15-150
Algo & Advanced Data Structures, 15-351

Statistical Computing, 36-350
Principles of Imperative Programming, 15-122
Designing Human Centered Software, 05-391

Practical Data Science, 15-388
Web Application Development, 17-437
Interaction Design Overview, 05-392

Skills

Programming Languages
- Python
- Scala

- SQL
- Ruby

Other
- Prefect
- Spark
- AWS EMR/EC2/S3

- Databricks
- Clickhouse
- Flask

Projects

Spotipy Weekly

Practical Data Science Final Project

My team and I built a model that analyzes a user’s entire Spotify collection, generates a classifier based on the audio features of the entire collection, and recommends brand new music every Friday. Check out our report here.

Utilized Python, scikit-learn, pandas, and Spotipy (Spotify python library).

May 2018

Dv8

CalHacks 3.0

Built a stock predictor using machine learning (predicted 3M Stock using an SVM model), sentiment analysis, and Nasdaq’s Data-On-Demand API.

Utilized Python and TensorFlow.
November 2016

Friends are Forever (F.A.F)

HackMIT

Built a web-application that generated relationship insights based on FB Messages (response time, sentiment, etc.) Parsed through millions of messages.

Utilized Utilized Python, NLP libraries, Node, and Flask.
September 2017