I am a Data Analyst specializing in Business Intelligence with a robust foundation in Applied Mathematics. I excel at bridging the gap between advanced statistical methods (Hypothesis Testing, GMM) and actionable business insights. My expertise involves transforming complex data into strategic visualizations and narratives that drive decision-making.
- 🔭 I’m currently working on: Optimizing sales funnels through rigorous A/B/n Testing and statistical analysis.
- 🎓 Background: University Instructor in Physics & Math, enabling me to break down complex concepts into clear Data Storytelling.
- 🔬 Research: Published author in Statistical Classification and Mathematical Modeling (MDPI).
- 💡 Soft Skills: Critical Thinking, Hypothesis-Driven Problem Solving, Cross-functional Collaboration.
Data Analyst (Project-Based) | TripleTen (Aug 2025 – Nov 2025)
- Executed 10+ full-cycle analysis projects, transforming ad-hoc business inquiries into actionable data assets regarding e-commerce and finance.
- Developed interactive BI dashboards in Tableau, enabling stakeholders to track critical metrics like LTV, CAC, and Conversion Rates.
- Orchestrated ETL processes using SQL and Python to clean and integrate data, ensuring high data integrity.
- Validated strategic hypotheses using statistical methods to identify inefficiencies and support go/no-go decisions.
University Physics Instructor & Research Mentor | Autonomous University of Aguascalientes (Jan 2025 – Present)
- Data Storytelling: Translated complex mathematical models into accessible insights for undergraduate audiences.
- Performance Metrics: Analyzed student performance data to optimize instructional strategies, resulting in a 30% improvement in pass rates.
- Research Supervision: Guided students through hypothesis formulation, data collection, and statistical validation of results.
- Analysis & Programming: Python (Pandas, NumPy, SciPy), R (ggplot2), SQL, Snowflake, MATLAB.
- Visualization: Tableau, Power BI, Matplotlib, Seaborn.
- Statistical Methods: A/B Testing, Hypothesis Validation (T-test, Mann-Whitney, ANOVA), Regression Analysis, GMM.
| Project | Description | Stack |
|---|---|---|
| Recommender System Impact | A/B Test Evaluation. Analyzed 423,000+ user events across EU, NA, APAC, and CIS regions. Validated negative impact of a new recommender system using Z-tests, preventing potential revenue loss by advising against deployment. | Python Pandas SciPy |
| Food App UX Analysis | A/A/B Testing. Analyzed 244,000+ events to validate font design changes. Utilized Mann-Whitney U tests with Šidák correction to ensure changes did not negatively affect the sales funnel. | Python Seaborn Stats |
| Call Center Efficiency | KPI Definition. Processed 53,000+ call records for 1,000+ operators. Identified underperforming agents and defined efficiency thresholds based on statistical percentiles to improve retention. | Tableau Python EDA |
| Galaxy Cluster Classification | Unsupervised Learning. Processed 71,000+ observations in a 6-dimensional space. Developed an optimized algorithm using Gaussian Mixture Models (GMM) and Mahalanobis distance for robust outlier rejection. | R GMM Statistics |
- MSc in Applied Mathematics (In Progress) | Autonomous University of Aguascalientes (Expected 2026)
- Professional Certificate in Data Analytics | TripleTen (2025)
- 400+ hours focused on SQL, Python, and Tableau applied to business scenarios.
- BSc in Applied Mathematics | Autonomous University of Aguascalientes (2019 – 2024)
- Spanish: Native
- English: B2 - Professional Working Proficiency
- French: B1 - Intermediate
I’m open to collaborations on Data Science projects and innovative research. Email Me