Hey there, I'm
Jack Chervet
Hi! I'm Jack. After graduating from UC Davis and moving to NYC for a couple of years, I'm now working as a software engineer in San Francisco. In my free time I like to explore the city, spend time with friends, and try as many new restaurants as possible. I’m passionate about entertainment, creative problem solving, and new technologies. Let's get in touch!
B.S. - Computer Science, June 2018
Davis quickly became a second home to me. I was lucky enough to transfer to the school from community college along with some friends I had made, and they pushed me to be the best student, mentor, and friend that I could be. Davis is where I realized just how broad of a field Computer Science is, and where I was connected with my first real programming jobs. It's where I found opportunities to study abroad and learn about other cultures, even as I learned more about the culture I lived in every day. It's where I made lifelong friends and pursued my passion. I'll always be thankful for the experiences Davis provided me. Go Aggies!
Disney Streaming Services · San Francisco, CA
July 2020 - Present
Currently, I'm working as a Software Engineer at Disney Streaming Services - the team behind Disney+ and ESPN+! I work on the Payments team, which involves working on integrations with third-party Payment providers (PayPal, credit card providers, etc) and optimizations across our payment and billing systems. With all of the success of the Disney+ platform, this has been an immeasurably exciting time to be a part of this fantastic team!
Yext · New York, New York
November 2018 - July 2020
At Yext, I worked as a Software Engineer on the Partner Engineering team. Yext gives businesses the ability to build strong relationships with their customers by allowing them to control every aspect of their customer-facing information across the web. On the Partner Engineering team, I built out new Yext Knowldege Network integrations (and maintained current ones) with publishers such as Google, Facebook, and Yelp. I also led development and technical design of multiple Yext App Directory integrations accross business-critical functions.
Oodles Corporation · Remote
April 2018 - June 2018
I was contracted to design and construct the Oodles Inc. website, including all design/visual work, e-mail systems and database (MySQL), as well as front-end UI/UX (HTML/CSS, JavaScript, jQuery). I built this website on Node.js, and I feel that this experience helped further develop my web programming skills both on the front-end and the back-end. I was able to apply what I learned in developing the TurboHedge platform to more efficiently design and develop this solo project.
TurboHedge, Inc · Pleasanton, CA
June 2017 - August 2017
I was contracted by TurboHedge Inc. to work on a small team to design and implement the TurboHedge web platform (Node.js). This work included implementation of proprietary algorithms, user and data management systems (MySQL), as well as front-end UI/UX (HTML/CSS, JavaScript, jQuery). This was my first introduction to more modern web development and in designing a web application from scratch. Over the course of three months I was able to become proficient in all of the languages, technologies, and packages used (Handlebars, Bootstrap, etc.) and personally implement the majority of the front-end work as well as a significant portion of the back-end infrastructure.
Amazon Alexa Skill
Recognized by Amazon as a top-grossing skill in December 2017 & December 2018
I created a skill for Amazon Alexa using Node.js, hosted in AWS Lambda. This skill was a quiz game in which users would be prompted by Alexa to either identify or complete a line from famous Christmas songs. The skill was recognized by Amazon as a top grossing Alexa skill in December 2017, organically generating over 6,300 unique users. In December 2018 Christmas Song Quiz was again recognized by Amazon as a top grossing skill, organically generating over 16,400 unique users.
Classifcation/Regression model in Python
Predicted default likelihood and severity with .653 MAE
In Python, I used PCA and additional feature construction techniques to clean a large dataset of customer data. I implemented a stochastic gradient descent classifier to binary classify likelihood of a customer defaulting on a loan, and when a default was predicted I used a gradient forest regressor to estimate the severity of the default.