Schezeen Fazulbhoy is a Software Engineer 3 at Adobe with eight years of experience building web and data-driven solutions that blend technical rigor with creative design. Trained at Georgia Tech (MS in Computer Science, Interactive Intelligence) and NMIMS (BTech CS), she has shipped features across frontend theming, dark-mode UX, and data pipelines—from styling CircuitVerse docs to implementing Twitter sentiment analysis tools that export insights to Excel. Her background spans roles in finance tech, academic teaching assistance, and MLH open-source projects, reflecting comfort moving between product engineering and community-driven development. Colleagues describe her as someone who deliberately marries aesthetics and engineering, producing interfaces and tools that are both usable and analytically robust.
8 years of coding experience
5 years of employment as a software developer
Bachelor of Technology - BTech B.Tech Computer Science, Bachelor of Technology - BTech B.Tech Computer Science at SVKM's Narsee Monjee Institute of Management Studies (NMIMS)
Master of Science - MS Computer Science (CS) | Specialization: Interactive Intelligence, Master of Science - MS Computer Science (CS) | Specialization: Interactive Intelligence at Georgia Institute of Technology
ICSE Science, ICSE Science at Bombay Scottish School,Mahim
ISC Science, ISC Science at Bombay Scottish School, Mahim
Contributions:13 commits, 3 PRs, 17 comments in 1 day
Contributions summary:Schezeen primarily focused on theming and styling the CircuitVerse documentation site. They implemented dark mode functionality by adding and modifying CSS files, including `darkTheme.css` and adjusting the main theme. They also made minor UI adjustments, such as changing the background color of table cells, and corrected indentation and code formatting within the HTML and CSS files.
Build Bots, Scrape a website or use an API to solve a problem.
Role in this project:
Data Scientist
Contributions:11 commits, 2 PRs, 8 comments in 18 days
Contributions summary:Schezeen implemented a Twitter sentiment analysis tool within the repository, demonstrating skills in data extraction, text processing, and sentiment classification. The contributions involve fetching tweets based on a hashtag and using the TextBlob library to classify them. The user developed a script using Python to analyze tweets and store the results in an Excel file.
solveapipythonscrapescraping-websites
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