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.
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
Computer Science MSc: 2016 - 2017
- 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
- 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
- Numerical analysis
- Linear programming
- Applied numerical analysis
- Constrained and unconstrained numerical optimization
- Parallel optimization
- 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.
September 2017 - Present day
Data scientist at Business Data Evolution.
Statistical modeling and predictive analysis in data science consulting company.
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.
October 2015 - May 2016
Data scientist at MetronHomo, Grupo Salinas.
Designed and built statistical/machine learning models for predictive and inferential analysis, and created data visualization dashboards for market research consulting.
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.
December 2013 – June 2014
Intern at Global Banking and Markets, HSBC.
Financial products pricing, financial products analysis, Excel spreadsheets creation for statistical analysis.
English: Native-like competence and accent