Summary
This document is a resume for Jannate Raddadi, a triple major in Computer Science, Applied Mathematics, and Data Science seeking a data analytics or consulting role. The central argument is that Raddadi’s technical skills—spanning statistical analysis, programming, and data visualization—can translate complex data into actionable insights and strategic decisions. The resume highlights experience as a Product Manager Summer Associate at Lastmile Retail, where Raddadi automated Google API onboarding workflows to reduce client setup time from 4 hours to 45 minutes, and as a Data Engineer Intern at Standard Oil Maroc, where ETL pipeline optimization cut processing time by 28%. Selected projects include a high-frequency trading feed handler in C++ that reduced latency by 35% and a financial strategy modeling system in Python that outperformed a market benchmark by 12%. A reader takes away that Raddadi combines hands-on technical implementation with product-focused data analysis, supported by proficiency in Python, SQL, Tableau, and machine learning tools.
Key concepts
- FIX/FAST protocol — An industry-standard protocol used by major financial exchanges for real-time market data feed processing.
- ETL pipeline optimization — The process of improving Extract, Transform, Load workflows, demonstrated by a 28% reduction in processing time at Standard Oil Maroc.
- Google API integrations — Automated workflows using GA4, GTM, and GSC APIs that reduced client setup time from 4 hours to 45 minutes per account.
- Sharpe ratio — A metric used to evaluate strategy performance by measuring risk-adjusted returns, applied in the financial strategy modeling project.
- Monte Carlo Simulation — A statistical method listed under technical skills for modeling uncertainty in data analysis.
- STEM OPT Eligibility — A three-year work authorization status for STEM graduates in the U.S., noted in the resume’s objective.
From the book
Jannate Raddadi 781-692-5966 • jannateraddadi2@gmail.com • STEM OPT Eligible (3 Years) OBJECTIVE Triple major in Computer Science, Applied Mathematics, and Data Science seeking a data analytics or consulting role where statistical analysis, programming, and data visualization are used to translate complex data into actionable insights and strategic decisions. EDUCATION Bachelor of Arts — Computer Science, Applied Mathematics & Data Science May 2026 Augustana College | Rock Island, IL Relevant Coursework Data Mining • Machine Learning • Database Systems • Statistical Modeling • Linear Systems • Numerical Methods • Data Visualization EXPERIENCE - Product Manager Summer Associate Summer 2025 Lastmile Retail | New York City • Partnered with the CEO to evaluate product roadmap priorities using…
Popular questions readers ask
- How do Jannate's triple majors and stated objective of "translating complex data into actionable insights and strategic decisions" manifest specifically in both the Product Manager and Data Engineer roles? Explain, using examples from the text, how this translation process would occur for someone unfamiliar with data science.
- For the "High-Frequency Trading Feed Handler" project, describe the technical challenges involved in achieving "sub-millisecond latency" using "multithreading and memory-mapped I/O." What specific problems do these technologies solve in this context, and what potential trade-offs might be associated with their implementation?
- Jannate consistently quantifies impact (e.g., "reducing client setup time," "reducing processing time," "increased system throughput"). Choose one of these quantifiable achievements and detail the steps you would take to *measure* and *verify* such an improvement in a real-world setting. What data would you collect, what metrics would be critical, and what confounding factors would you need to control for to confidently attribute the change to your work?
- The resume lists both "Machine Learning" and "Statistical Methods" among skills and coursework. How do these distinct yet related approaches appear to be applied in Jannate's work (e.g., in projects like "Financial Strategy Modeling" versus general analytics)? Explain the fundamental difference in problem-solving methodology between evaluating trading strategies with historical data and using a "Random Forest" model for a predictive task.
- In both the Product Manager and Data Engineer roles, Jannate emphasizes using data visualization to "inform dashboard updates" and "visualize operational metrics for leadership decision-making." Beyond merely creating a graph, what critical considerations and design principles must be employed to ensure a visualization genuinely translates complex data into *actionable insights* and *strategic decisions* for a non-technical audience, rather than just presenting information?