The role involves analyzing complex data sets, develop machine learning models, and extract insights to inform business decisions. I work with both structured and unstructured data, including big data, and use a variety of statistical and machine learning techniques to identify trends, patterns, and relationships in the data.In my first project I worked with chargebacks, where I analyzed transaction data to identify instances of fraud and errors. Using machine learning, our team were able to identify patterns and anomalies in the data that allowed to identify specific instances of fraudulent activity and recommend changes to the transaction processing system from prediction of the outcome of the arbitration.Another project I worked on involved trend detection in search data, where our team analyzed search queries to identify emerging trends in consumer behavior. Using heuristic models, we were able to identify patterns and anomalies in the data that allowed to predict future trends and recommend linked items to build offer pages.Throughout my work, I use a variety of modeling techniques, including heuristic, statistical, and artificial intelligence-based models, depending on the nature of the problem and the data available. The models may be used for a variety of purposes, including predicting customer behavior, optimizing marketing campaigns, and improving supply chain efficiency.

Assistant professor in the Department of Inorganic, Analytical and Physical Chemistry (DQIAyQF) FCEN-UBA

Research within the framework of "Beca Estímulo" (UBA)

Project in Kaggle:- Exploratory analysis of the data, applyingstatistical concepts and visualizations. use ofthe associated libraries in Python (Seaborn,Matplotlib and SciPy).- Manipulation, transformation and preparation of datawith Pandas.- Training, implementation and evaluation ofMachine Learning models, using the libraryScikit Learn.

Subject exercise Data Laboratory (Data Sciences - UBA):-Use of dimension reduction techniques (PCA)- Exploratory data analysis, applying visualizations (Matplotlib).- Training, implementation and evaluation ofMachine Learning models, using the libraryScikit Learn.

Project in Kaggle:- Exploratory analysis of the data, applyingstatistical concepts and visualizations. use ofthe associated libraries in Python (Seaborn,Matplotlib and SciPy).- Manipulation, transformation and preparation of datawith Pandas.-Use of class balancing techniques (Random Under Sampling and SMOTE)- Training, implementation and evaluation ofMachine Learning models, using the libraryScikit Learn.