I have a degree in Applied and Computational Mathematics from UFRGS and a specialization in Data Science and Big Data Analytics from Estácio. My current professional goal is to solve business problems through analytical and computational methods. Among my academic and professional journey, I can highlight - in general - the following skills:💻 Strong foundation in computing: data structures and algorithms (search algorithms, greedy algorithms, dynamic programming and combinatorial optimization - linear and integer programming, simplex method, metaheuristics).📄 Data analysis: use of Python and its data tech stack (Pandas, Matplotlib, Seaborn), familiarity with Power BI and Excel for quick dashboards, and SQL (MySQL, PostgreSQL).🎲 Probability: frequentist interpretation at the level of statistics/mathematics.📊 Elementary statistics: descriptive measures, main estimators, traditional linear models such as linear regression and logistic regression, hypothesis testing and ANOVA.🤖 Automation and Web Crawlers: development of web crawlers, web automation and low-code automation with Power Automate.I am currently developing the following skills both theoretically and practically:⚙️ Machine Learning, supervised (linear models, decision tree, kNN, Naive Bayes) and unsupervised (k-means clustering, db-scan and PCA). I ​​have practical experience in both personal projects and professional experience. In this case, working with traditional tabular data using the Python stack: sklearn, statsmodels and PyMC. For unstructured data, I am familiar with the Hugging Face library for neural network models involving PyTorch or TensorFlow.🌐 Cloud Computing, such as Microsoft Azure, AWS (Amazon Web Services) and GCP (Google Cloud Platform). I have experience and some familiarity with some platforms such as IBM Cloud, Azure and GCP. Highlighting IBM cloud where I have some certifications for its data use case (watsonx, Cloud Pak for Data).📈 Advanced Methods of Statistics/Econometrics, here I refer to several statistical models/methods, with a certain semblance to Machine Learning, but under the following scope: the generalized linear model and its special cases, time series analysis (ARIMA and variants) and structural equations of econometrics (especially factor analysis). As for statistical methods, I include the estimation methods for those models: maximum likelihood estimation, generalized method of moments and Bayesian estimation (intensive methods involving MCMC).