## CV

This is an online version of my education, skills and work experience. You can find a shorter PDF version here.

My main interests are statistical modeling, predictive analytics, data visualization and machine learning.

# Skills

**Statistics:**
Generalized Linear Models, Bayesian statistics, principal component analysis (PCA), spatial statistics, Bayesian networks, clustering, hierarchical/multilevel modeling, A/B testing

**Machine Learning:**
Random Forest, neural networks, support vector machines, recommender systems, market basket analysis, association rules, artificial neural networks, deep learning, convolutional neural networks, recurrent neural networks

**Finance:**
Interest rates, financial derivatives, fixed income products, structured finance, VaR

**Programming:**
R, C++, C, Java, MATLAB, SQL, Python, LaTeX, Bash

**Other technologies:**
Apache Spark, Apache Hadoop, Apache Hive, Keras, Tensorflow

**Other:**
Data mining, Natural Language Processing, image processing, text mining, linear programming

# Work experience

## October 2018 - Present day

**Data scientist at Banco Azteca.**

Analyze data for the marketing and CRM teams of the bank.

- Campaign analysis and A/B testing.
- Predictions of future bank transactions.

## September 2017 - September 2018

**Data scientist at Business Data Evolution.**

Statistical modeling and predictive analysis in data science consulting company.

- Performed statistical analysis, exploratory data analysis and developed models in R.
- Created data visualization dashboards using Rstudio’s Shiny.
- Analysis of predictive models using Individual Conditional Expectation (ICE) and variable interaction quantification.
- Deployed final predictive models in Microsoft Azure VM and Azure Machine Learning.
- Developed Bayesian predictive model for small black and white documents belonging to 4 classes.
- Developed a Bayesian time series demand prediction model for a national grocery store.
- Met with clients to understand their problems and needs, as well as presenting intermediate and final results.

## May 2016 - June 2017

**Data scientist at CAD Salud.**

Created predictive and inferential statistical models, as well as data visualization dashboards for data projects aimed toward health problems.

- Performed statistical analysis, exploratory data analysis and developed models in R.
- Created data visualization dashboards using Rstudio’s Shiny.
- Created simple predictive model for potential diabetes patients in public hospitals.
- Generated spatial mathematical model for vaccines demand in public hospitals in Mexico.
- Aided in the creation of multivariate index to rank public hospitals.

## October 2015 - May 2016

**Data scientist at Grupo Salinas.**

Designed and built models for predictive and inferential analysis, and created data visualization dashboards for market research consulting.

- Performed statistical analysis, exploratory data analysis and developed models in R.
- Created data visualization dashboards using Rstudio’s Shiny.
- Aided in creation of predictive model of loan origination clients in a Mexican bank.

## June 2015 – August 2015

**Junior Analyst at Revenue Management, Best Day.**

Summer project involving customer analysis and market segmentation using association rule learning (market basket analysis).

## June 2014 – June 2015

**Data analyst at Computer Research and Analysis Laboratory, ITAM.**

Machine learning application in Natural Language Processing for automation in news classification for media monitoring company.

- Cleansed news articles data for modeling, mainly using R.
- Created different predictive models for automatic news classification in Python using Scikit Learn.

## December 2013 – June 2014

**Intern at Global Banking and Markets, HSBC.**

Financial products pricing, financial products analysis, Excel spreadsheets creation for statistical analysis.

# Education

### Computer Science MSc: 2016 - 2017

Relevant coursework:

- Machine learning
- Bayesian generalized linear models
- Bayesian multilevel modeling
- Computational statistics
- Data product architecture
- Analysis of algorithms
- Programming languages
- Complexity theory
- Advanced operating systems
- Distributed systems
- Computer architecture
- Compiler design

Note: Took around 70% of the courses in the **Data Science master’s program** at ITAM.

### Applied Mathematics BSc: 2010 - 2015

Relevant coursework:

**Statistics:**- Probability theory
- Mathematical statistics
- Stochastic processes
- Linear regression
- Multivariate statistical methods
- Bayesian statistics
- Statistical learning
- Generalized linear models
- Spatial statistics

**Computer Science:**- Algorithms and programming
- Data structures
- App development
- Data mining

**Optimization:**- Numerical analysis
- Linear programming
- Applied numerical analysis
- Constrained and unconstrained numerical optimization
- Parallel optimization

**Math:**- Integral and differential calculus
- Linear algebra
- Real analysis
- Dynamical systems

Thesis: Implementation of a recommender system based on matrix factorization and stochastic gradient descent. You can find the code here and the final PDF (in Spanish) here.

# Languages

**Spanish:**
Native

**English:**
Native-like competence and accent

**French:**
Basic