Applied Statistics & Applied Mathematics student at American University
Washington, D.C. · LinkedIn · GitHub · js0596a@american.edu

I build data products that turn messy operational data into decisions. My work sits at the intersection of applied statistics, production analytics, simulation, dashboarding, and practical software tools for real business workflows.

Native Android inspection app for leather load tracking, editable defect catalogs, photo evidence, and executive PDF reporting.

• Built an offline Android prototype using Java, SQLite, and native Android APIs.
• Designed load registration, duplicate prevention, defect capture, photo workflows, filtering, local persistence, and email/PDF sharing.
• Added process-based reporting for Incoming and Liberación calidad, with KPI cards, defect distribution charts, and photo-backed summaries.

Dash + SimPy operations research tool for production-flow simulation, plant cost modeling, SPC, capability analysis, and Bayesian process classification.

• Built Python dashboards and simulation workflows from production logbooks to analyze throughput, bottlenecks, machine capacity, and process costs.
• Compared scheduling policies, lot-splitting strategies, service-time risk, and machine/server capacity assumptions.
• Added interactive views for Gantt timelines, control charts, capability metrics, cost summaries, and process classification.

Local Dash application for leather production KPI tracking and MLP-based area forecasting.

• Built an Excel-upload workflow with automatic English/Spanish column mapping for leather operations data.
• Added KPI views for area, pieces, families, leather types, weekly trends, and production mix.
• Integrated model training and inference so users can train an MLP on their own local dataset and run predictions from the app.

Interactive Dash portfolio that turns my resume, projects, skills, awards, and experience into an explorable web app.

• Designed a portfolio companion to a static PDF resume with searchable project and experience sections.
• Showcases production analytics, operations research, machine learning, Power BI, and dashboarding work.
• Built with Python, Dash, Plotly, Pandas, CSS, Docker, and pytest.

AI meal-planning prototype for affordable, personalized nutrition. Placed 1st in the UN/FAO track.

• Helped build a workflow using health, dietary, budget, and location inputs to generate personalized meal plans and grocery lists.
• Contributed to product/data design for a prototype focused on food insecurity, nutrition equity, and budget-aware planning.

Strategic asset allocation case for a $2.4B endowment under liquidity, real-return, and ethical-divestment constraints.

• Led a five-member team evaluating near-term funding needs, allocation tradeoffs, and scenario risks.
• Supported recommendations with liquidity analysis and stagflation scenario testing.

American University, College of Arts and Sciences — Washington, D.C.
B.S. in Applied Statistics and Applied Mathematics · Expected May 2027
GPA: 3.91

Relevant coursework: Linear Algebra, Multivariable Calculus, Probability Theory, Mathematical Statistics, Regression Modeling, Data Mining, Machine Learning, Algorithms & Data Structures, Numerical Methods, Statistical Machine Learning.

León, Guanajuato, Mexico · May 2026 – Aug 2026

• Built Python dashboards and SimPy workflows from production logs to analyze throughput, bottlenecks, machine capacity, and process costs.
• Performed EDA, data cleaning, and statistical modeling on plant process data for queueing studies, capability reviews, and production planning.
• Developed operations tools including queueing simulation, control charts, process capability studies, and Bayesian process classification.
• Prototyped an Android inspection tool for leather load tracking, defect catalogs, photo evidence, and PDF reporting.

Washington, D.C. · May 2025 – Aug 2025

• Built Python and Power BI workflows to clean, join, and transform pricing, demand, and market data across three domains for $2.1B market sizing.
• Modeled a $2.1B market opportunity using exploratory review and scenario planning to identify demand drivers and high-value segments.
• Translated business questions into KPI definitions, data requirements, and Power BI reporting logic for repeatable workflows.

Product & Data Contributor · Washington, D.C. · Apr 2026

• Placed 1st in the UN/FAO track by helping build FoodBridge, an AI meal-planning app for affordable, personalized nutrition.
• Designed a five-step data workflow using health, dietary, budget, and location inputs to generate personalized meal plans and grocery lists.
• Pitched a working prototype addressing food insecurity, nutrition equity, and budget-aware meal planning.

Team Lead · Washington, D.C. · Aug 2024 – May 2025

• Led a five-member team developing strategic asset allocation recommendations for a $2.4B endowment under liquidity, real-return, and ethical-divestment constraints.
• Evaluated $290M in near-term funding needs and supported portfolio recommendations with liquidity and stagflation scenario testing.

Languages & Tools: Python, SQL, R, Java, Excel, Power BI, Git/GitHub, Docker
Python/Data: Pandas, NumPy, Plotly, Dash, SimPy, scikit-learn, TensorFlow/Keras, PySpark
Methods: Data cleaning, EDA, KPI reporting, dashboarding, market sizing, cost modeling, simulation, queueing methods, SPC, regression, machine learning, scenario planning
Languages: English, Spanish, German

• Building production analytics tools for manufacturing operations.
• Turning plant-floor data into dashboards, simulations, and decision-support models.
• Developing practical software prototypes that reduce manual reporting and improve operational visibility.