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Work Background
Inspired Education GroupEducational Data Analyst/Teacher
Mar. 2024Collect educational data from our schools, using educational systems. Manage and analyze the data, creating dashboards and presentations that are easy to understand to generate insights.
EBAC - Escola Britânica de Artes Criativas e TecnologiaData Scientist
Nov. 2023 - Jan. 2024Rio de Janeiro, BrazilPredictive pricing Modeling with Decision Trees: This data science project employs regression tree models to predict asset values. Technologies: Python/R, Jupyter Notebook, Git/GitHub.
Pontifícia Universidade Católica do Rio de JaneiroData Scientist/ Data Engineer
Jul. 2023 - Oct. 2023Rio de Janeiro, BrazilLogistic Efficiency & Customer Satisfaction Study - Olist E-commerce Data engineering project focused on addressing logistic efficiency and customer satisfaction challenges in Olist's e-commerce operations. Analyzed delivery times, customer satisfaction metrics, and product/seller performance to enhance the overall shopping experience. Technologies: Python, Pandas (for data manipulation and analysis), Google Cloud Storage, Google Colab, Jupyter Notebooks, Data Visualization ( libraries: Matplotlib, Seaborn), Data Wrangling, ETL (Extract, Transform, Load) Processes, API usage, GitHub (for code versioning and sharing) and Machine Learning.
Pontifícia Universidade Católica do Rio de JaneiroMachine Learning Analyst
May. 2023 - Jul. 2023Rio de Janeiro, BrazilTelecommunications Churn Analysis Project Project focused on analyzing churn within the telecommunications industry. Utilized machine learning techniques to predict customer turnover based on factors like contract type, payment methods, and customer satisfaction. Developed strategies for reducing churn through loyalty programs, personalized offerings, and targeted communications. Strengthened skills in data analysis, machine learning, and strategic problem-solving. Technologies & Tools: Python (Programming Language),Pandas (Data Manipulation Library,NumPy (Numerical Computing Library),Matplotlib (Data Visualization Library),Seaborn (Statistical Data Visualization Library), Scikit-learn (Machine Learning Library), Jupyter Notebook or Google Colab (Interactive Computing Platform), GridSearchCV (Hyperparameter Optimization Tool), RandomForestClassifier (Machine Learning Algorithm), LogisticRegression (Machine Learning Algorithm)
Pontifícia Universidade Católica do Rio de JaneiroDeep Learning Researcher
May. 2023 - Jul. 2023Rio de Janeiro, BrazilExperience: Sentiment Analysis on Airlines' Tweets. Project focusing on sentiment analysis to understand customer opinions and emotions regarding airlines' products and services. Developed a neural network model to classify tweets about specific airlines as "Positive-neutral" or "Negative," aiming to provide valuable insights for reputation enhancement and customer service improvement. Hypothesized that customer satisfaction correlates with the frequency of negative comments, often associated with poor service and delays. Conducted analysis exclusively on Twitter comments. Technologies: Sentiment Analysis, Natural Language Processing (NLP), Python Programming, TensorFlow and Keras for Neural Networks, Deep Learning: LSTM (Long Short-Term Memory) Networks, Data Preprocessing with Tokenizer and pad_sequences, Data Visualization with Matplotlib and Seaborn, Data Manipulation with Pandas, Machine Learning: Classification Analysis Model Optimization and Hyperparameter Tuning, Model Evaluation: Accuracy, Loss, Confusion Matrix Working with Text Data: Regular Expressions (re), Working with Jupyter Notebook or Google Colab nltk library for text processing (WordNetLemmatizer, stopwords), scikit-learn for feature extraction (CountVectorizer) and model selection (train_test_split), Use of GitHub for version control
Pontifícia Universidade Católica do Rio de JaneiroData Analyst
Feb. 2023 - May. 2023Rio de Janeiro, BrazilTelecommunications Churn Analysis Project: A project focused on analyzing churn within the telecommunications industry. This involved utilizing machine learning techniques to predict customer turnover based on various factors such as contract type, payment methods, and customer satisfaction. By identifying behavior patterns and cancellation risks, we developed strategies to reduce churn through loyalty programs, personalized offerings, and targeted communications. This experience honed my skills in data analysis, machine learning, and strategic problem-solving. Technologies: Python, NumPy, Pandas, Seaborn & Matplotlib, Scikit-learn, Machine Learning (Supervised Learning).
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