Jessie Ifeanyi

Curious, collaborative, and results-driven data professional with a strong foundation in analytical reasoning, structured problem-solving, and continuous learning. Proven ability to design and implement scalable data pipelines, optimize workflows, and deliver insights across fintech, engineering, and research domains. Self-directed and dependable in autonomous and team-based settings.— whether I'm designing scalable data pipelines, optimizing financial models, or leading open-source contributions. Hands-on experience in Python, SQL, Airflow, Docker, and AWS. I’ve led projects ranging from urban evacuation optimization to portfolio simulation and Creative Commons data infrastructure — always with a focus on clarity, maintainability, and results.

I believe in thoughtful engineering, transparent documentation, and empowering teams through reproducible workflows.

- Creative Commons Quantifying Project – Lead Developer Oct 2025

• Designed and implemented the Internet Archive data pipeline
• Built normalization logic for licenses and languages with reproducible diagnostics
• Integrated session management, retry logic, and error handling
• Delivered clean, auditable outputs aligned with open standards
• Tools: Python, Internet Archive API, Flake8, Black, isort, pipenv, pre-commit

- Portfolio Optimization Case Study Oct 2025

• Compared equal-weighted vs optimized portfolios using Monte Carlo simulation and mean-variance optimization
• Achieved >50% annual return with Sharpe ratio >1.65
• Tools: Python, Pandas, NumPy, Matplotlib, yFinance, SciPy, Plotly

- Urban Evacuation Optimization – Master’s Thesis Jul 2025

• Developed network flow models for evacuation and capital optimization
• Applied graph theory and numerical methods
• Tools: Python, GurobiPy, GAMA, Pandas, NumPy, Matplotlib

- Smart Lead Value Prediction – Uptain GmbH 2023 – 2024

• Built regression model and automated deployment pipeline for lead prioritization
• Tools: Python, SQL, Docker, Airflow, AWS Lambda

- Quality Classification of Molded Products – UnivAQ Dec 2023 – Feb 2024

• Applied ML classification to improve defect detection accuracy
• Tools: Python, Scikit-learn, Pandas, Matplotlib

- Wind Energy Forecasting – University of Hamburg

• Modeled wind efficiency using cubic power curve and 5PL model
• Improved forecast accuracy by 22%
• Tools: Python, NumPy, SciPy, Matplotlib, Pandas

- Prosper Loan Data Visualization – ALX-T Udacity

• Built interactive dashboards to analyze loan performance trends
• Tools: Python, Pandas, Matplotlib, Seaborn, Plotly

- Financial Risk Modeling – Osagbobu Bright & Co

• Conducted credit risk analysis for small business lending
• Tools: Excel, Python, Pandas, Financial Modeling Toolkit

- WeRateDogs Data Wrangling – ALX-T Udacity

• Standardized pipeline for 5,000+ tweets for sentiment analysis
• Tools: Python, Pandas, Tweepy, JSON, Regex

- Medical NoShow Analysis – ALX-T Udacity

• Investigated factors influencing patient no-shows
• Tools: Python, Pandas, Matplotlib, Seaborn

- Flask App Deployment – ALX-T Udacity

• Deployed Flask app using Docker, Kubernetes, AWS EKS
• Tools: Flask, Docker, Kubernetes, AWS EKS

- Coffee Shop App – Role-Based Access Control

• Implemented Auth0-secured REST API with role-based permissions
• Tools: Flask, Auth0, Postman, SQLAlchemy

- Trivia API – Flask Game App

• Built PostgreSQL-backed trivia API with robust error handling
• Tools: Flask, PostgreSQL, Python, SQLAlchemy

- Fyyur App – Artist Booking Platform

• Developed CRUD operations for artist-venue matchmaking
• Tools: Flask, PostgreSQL, SQLAlchemy, Python