Work experiences

Data Science Junior 

May 2023 - October 2023

• Collect, clean and analyze data with Python and its libraries (pandas, NumPy).
• Perform exploratory data analysis (ETL, EDA) identifying trends and patterns, optimizing business strategies.
• Develop, train and optimize predictive and Machine Learning models (supervised, unsupervised, reinforced).
• Generate advanced reports and visualizations with PowerBI and Python (Matplotlib, Seaborn), for global visualization of results in a clear and concise way.

Target: Provide solutions in the area of analytical marketing and business development from advanced analytics, for products in the financial sector.

For this, different projects were carried out, starting with data analysis, ETL and EDA data to generate a data visualization dashboard in Power Bi. Subsequently, Machine Learning models of Propensity, Segmentation (clustering) were made in order to give recommendations for improvement to the company, about which were the best MKT channels to impact, best purchase probabilities for each customer by product, among others.

In addition, outside the project as such, different machine learning and artificial intelligence models such as Data Science were carried out, among which we can highlight Data Engineering, Supervised ML, Unsupervised ML, Deep Learning and final project to boost sales and marketing strategies for a company.

For this purpose, different technologies, languages and tools have been used; some of them are listed below

• Models: logistic regression, linear regression, time series, clustering, and deepLearning.
• Languages and libraries: Python, Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn, SQL.
• Algorithms: KMEANS, XGBoost, Decision Tree, Random Forest.
• Data visualization: Power BI, Tableau.
• Tools: jupyter notebook, Google Suite (Google colab, Google Drive), Visual studio code, Git, GitHub, Mysql workbench, Airflow, Docker.