Full Stack Engineer, Data Scientist
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I'm a Computer Science student at the University of Michigan, set to graduate in 2025. My area of expertise lies in using data and algorithms to resolve complex problems in the finance and tech sectors.
I'm interested in how data can guide smarter decisions in business. I've used regression analysis on financial statements and balance sheets to identify growth opportunities, and I've also optimized algorithms to increase their efficiency and reliability.
I enjoy working in fast-paced environments and am committed to advancing my skills and pushing the boundaries of what's possible in fintech and technology.
Feel free to connect with me for collaborations, opportunities, or discussions about the future of data-driven technologies.
Professional Summary (CS / Full-Stack / Data Science Focus)
Full-stack data engineer & ML developer with 2+ years turning raw, high-velocity data into production-grade software and predictive models. Core stack: Python, TensorFlow, SQL, Power BI, Azure Data Factory, Git, Docker.
Algorithmic trading platform (self-directed, 2023 – present): built end-to-end pipeline—data ingestion, feature engineering, ensemble ML, and automated risk controls—that back-tests to a 70 % win rate and >200 % cumulative return over five years. Designed REST micro-services and CI-driven testing to keep latency sub-50 ms.
Data Analyst, Pactiv Evergreen (Summer 2024): automated ETL scripts that cut manual prep 75 %; engineered real-time KPI dashboarding in Power BI/DAX and Azure Data Factory, halving reporting cycle time and enabling daily oversight of $50 M+ in shipments across 20+ DCs.
Data & CS Intern, Triple Point (Summer 2022): ran regression analyses on portfolio companies, influencing follow-on investments; refactored database schema and querie.
Leadership & risk management: as Risk Management Chair (2022-2023) created chapter-wide safety program and managed associated budget—experience that translates to disciplined project and production controls.
Sep 2020 – Jan 2025
