Resume

Summary

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Data analyst who turns messy business data into decisions with Python, R, SQL and Tableau. Built a Python billing audit covering nearly $1M in invoices for a small business, and presented datathon findings to judges from Microsoft and Resolution Economics. B.S. in Business Analytics from CSUN with a 3.93 GPA (top 10% of class).

Experience

Dent Right · Los Angeles, CA

Operations Coordinator (Sep 2020 – Present)
  • Data Pipeline & Audit: Built a Python pipeline that extracted ~7,900 invoice records from 46 months of client statements (nearly $1M billed) and reconciled every statement, flagging 40 billing records for correction, including 3 possible double billings. See the project →
  • Reporting & Dashboards: Built an interactive billing dashboard tracking revenue by month, client, service and year, giving the owner visibility into trends that weren't visible before.
  • Client Analytics: Analyzed billing across 6 dealership accounts, found that one account drives 56% of revenue, and recommended steps to reduce client concentration risk.
  • Accounts Receivable: Manage end-to-end billing, invoicing and payment processing; reconcile payments against outstanding invoices and resolve discrepancies and overpayments.
  • Financial Reporting: Prepare monthly financial summaries and cash-flow tracking for owner review.
  • Operations: Coordinate payroll payments, bank deposits, vendor purchasing and procurement using Microsoft 365.

Rocket · Industry Mentorship

Data Analyst Mentee (Summer 2026)
  • Machine Learning: Built a scikit-learn regression pipeline (scaling, one-hot encoding) to predict nightly prices for 48,895 NYC Airbnb listings; methodology and business relevance reviewed by a Rocket data analyst. See the project →
  • AI-Assisted Development: Developed the pipeline with Claude Code as part of the mentorship's focus on industry AI tools, directing the workflow and reviewing and validating the code and model results.
  • Data Cleaning: Removed 1,055 outlier rows, imputed missing review rates and log-transformed the skewed price target.
  • Results: Reached a test R² of 0.53 (RMSE ≈ $69); private rooms priced ~53% below entire homes and Manhattan ~71% above the Bronx, all else equal.

CSULB Datathon · Microsoft

Datathon Competitor (Spring 2026)
  • Data Sourcing & Analysis: Sourced 2000–2023 national, state and industry data from the U.S. Bureau of Economic Analysis and built time-series and comparative analyses in Python (pandas, matplotlib, seaborn) on a 5-person team. See the project →
  • Insights: Found GDP outpaced personal income by 20–30 index points since 2000, with state per-capita income ranging from $45K to $88K.
  • Stakeholder Presentation: Presented findings in an 8-minute pitch and Q&A to a judging panel that included Microsoft professionals.

LMU Datathon · Resolution Economics

Datathon Competitor (Spring 2026)
  • Operational Analysis: Analyzed 1,732 emergency airlift calls across 21 helicopter bases to measure response times (44.6-min average), peak call hours and base workloads. See the project →
  • Compliance Modeling: Modeled a 3-break meal schedule in Python and estimated ~31% of shifts would violate California meal-break rules, up to 55% at the busiest base.
  • Recommendations: Quantified a 3.4-minute response-time cost of rerouting using geodesic distance (geopy), and presented staffing and staggered-break recommendations to Resolution Economics.

Selected Projects

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  • Turning Around a Kitchenware Brand Benchmarked a declining kitchenware firm against two competitors with Tableau dashboards, seasonality and RFM analysis; traced a $210K revenue decline to a 26% drop in its catalog channel and recommended a three-part channel strategy.
  • Pricing Against the Competition at Donna's Gas Forecast daily gas sales (962 gallons/day) and built a multiple regression in R (R² 0.96) to recommend prices under three competitor scenarios.
  • Which Video Game Publishers Win, and Where? Analyzed decades of video game sales in R and Tableau, showing EA leads sports, Activision leads shooters and Nintendo leads Japan; exponential-smoothing forecasts put EA in the strongest position for the next five years.
  • How Twitter Feels About Bitcoin Ran NRC and Bing sentiment analysis on Bitcoin tweets in R; positive words outnumbered negative 3,794 to 2,193, and I recommended an entry strategy that tracks negative sentiment as an early warning.
  • Mapping the Language of Disaster Tweets Built a word co-occurrence network from 1,500 disaster tweets in R (tm, igraph) and used community detection to recommend marketing language for an emergency-supplies brand.
  • Segmenting Wine Buyers with k-Means Segmented wine customers with k-means in R and mapped each segment's buying season in Tableau, showing campaigns could be timed to each group instead of sent to everyone at once.
  • Predicting Bank Customer Churn with a Neural Network Trained a neural network in R to flag likely account closures and profiled the customers leaving: older (45 vs. 37) with higher balances, a valuable group to prioritize for retention.
  • What Drives Life Expectancy? Tested socioeconomic drivers across 2,928 country-year records in Excel and Tableau; schooling (r = 0.72) and adult mortality (r = –0.70) were the strongest factors.

Writing & Research

Read the paper
  • The Role of Analytic Thinking in Resisting Misinformation and Propaganda on Social Media. Formal research report synthesizing psychology and data science studies on misinformation susceptibility; compared education-based and machine learning solutions and recommended human-in-the-loop oversight.

Education

California State University, Northridge

Northridge, CA
Bachelor of Science, Business Administration — Business Analytics
GPA 3.93 · Top 10% of class · Beta Gamma Sigma International Business Honors Society
Recruitment Chair, Latino Business Association · Marketing Chair, Business Analytics Club
CSUN

Pasadena City College

Pasadena, CA
Certificate of Achievement, Business Analytics
PCC

Technical Skills & Tools

Languages & Libraries: Python (pandas, NumPy, scikit-learn, matplotlib, seaborn), R (tidyverse, ggplot2, tidytext), SQL

Analytics & Modeling: Regression, classification, k-means clustering, neural networks, time-series forecasting, sentiment analysis, hypothesis testing

Visualization & BI: Tableau, Excel, dashboard design, data storytelling

Tools: Claude Code (AI-assisted development), Jupyter, Google Colab, RStudio, VS Code, Microsoft 365

Core Competencies: Data cleaning, exploratory data analysis, KPI reporting, financial reconciliation, stakeholder presentations

Relevant Coursework

  • CSUN · Data Visualization
  • CSUN · Predictive Analytics
  • CSUN · Machine Learning
  • CSUN · Business Analytics (Excel & Tableau)
  • CSUN · Marketing Analytics
  • CSUN · Report Writing
  • PCC Business Analytics Certificate · R, SQL, forecasting and machine learning

Browse my class notebooks →

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