Yousaf Abbasi
Software Engineering Intern at ShipBots
Applied Statistics and Data Analysis at Carnegie Mellon University Qatar
United States
Hi, I'm Yousaf Abbasi!
Software Engineering Intern at ShipBots
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ShipBots
Software Engineering Intern
August 2025 - Present
Automated sales flows using Python, SQL, REST APIs, cutting manual data entry by 30% and boosting accuracy
Integrated OpenAI GPT APIs with CRM to improve AI outreach and client follow-ups by more than 15%
Deployed AWS Lambda cloud functions syncing AI pipelines with 3PL logistics to reduce reporting delays 20%
Applied Agile/Scrum, Git, CI/CD pipelines to raise CRM data quality and shorten deal cycles by nearly 20%
EJoule
Software Engineering Intern
May 2025 - August 2025
Implemented Python/SQL pipelines for inventory tracking, raising forecast accuracy by 20% company-wide
Delivered real-time stock dashboards using AWS S3, Lambda, Power BI, cutting manual reporting tasks by 15%
Designed Tableau visualizations with 10+ KPIs enabling more data-driven leadership decisions company-wide
Performed unit testing and peer code reviews to improve code reliability, thus reducing deployment errors by 12%
Carnegie Mellon University
Computing Services Assistant
September 2024 - May 2025
Resolved 50+ weekly IT tickets using Bash, ServiceNow, Active Directory, cutting incident resolution time 20%
Created knowledge-base wikis/scripts that proposed fixes and lowered repeat service requests by more than 25%
Diagnosed network, email, and authentication issues to keep university computing systems of 10,000+ users secure
Saved 3 hours weekly in escalated support time by collaborating with IT teams to resolve 75+ high-priority tickets
amPICQ
Software Engineering Intern
July 2024 - August 2024
Developed Python and C/C++ simulation tools, boosting testing efficiency 25% and reducing validation cycles
Proposed quantum key distribution (QKD) algorithms and 5G/QRNG prototypes for secure communications
Built modular software components with Git/GitHub, decreasing integration time by more than 3 hours per week
Applied statistical modeling to analyze photonic IC simulation outputs to boost model prediction accuracy by 7%
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