Data Scientist2019 - PRESENTReforestum
Technologies: Computer Vision, Docker, Pandas, Data Science, Mathematical Modeling, Machine Learning, Mapbox, TensorFlow, Python
- Developed deep learning models that monitor forestry areas using satellite images. The final goal is to monitor forest conditions and to calculate carbon stocks in real-time.
- Implemented a back end that performs ETL, machine learning modeling, and serves the results via an API.
- Visualized GIS results via Mapbox.
- Trained and deployed machine learning models in AWS.
Data Scientist Consultant2020 - 2020Minsait
Technologies: Docker, Pandas, Data Science, Mathematical Modeling, Machine Learning, Git, Dash, Python
- Identified potential business cases where machine learning models could bring value to the clients.
- Participated in the research, development, and implementation of predictive maintenance models of ATMs for one of the biggest banks in Europe.
- Developed dashboards and interactive graphs.
Data Scientist2019 - 2019TecDeSoft
Technologies: Pandas, Data Science, Mathematical Modeling, Machine Learning, Optimization, SQL, Python
- Created business value with available data from existing clients, focusing on data-driven action.
- Implemented predictive maintenance of factory areas and optimal scheduling of hydroelectrical power plants.
- Taught my colleagues the principles of data science and machine learning.
Data Scientist2016 - 2018Siemens Gamesa
Technologies: Pandas, Data Science, Mathematical Modeling, Machine Learning, SQL, MATLAB, RStudio Shiny, R
- Developed a framework consisting of a database, an app, and a dashboard in order to let users predict the performance of wind turbines.
- Analyzed data from measurements and detected possible misalignments with the expected behavior.
- Extracted meaningful information from multi-dimensional datasets.
- Communicated the results and statistical terms to electrical engineers and sales officers in a clean and concise manner.
- Read, studied, and kept up to date with current ISO standards.
- Quantified the risk of different warranty strategies.
PhD Candidate2013 - 2016Technical University of Denmark
Technologies: Data Science, Mathematical Modeling, Operations Research, Machine Learning, R
- Developed models for decision making under uncertainty, based on stochastic optimization techniques.
- Built-up a new type of forecasting modeling framework that was based on inverse optimization techniques and machine learning principles.
- Extensively used R for data processing and GAMS-CPLEX for building optimization models.
- Used a cloud computing framework for parallelizing the calculations.
- Presented the research topics and results in international conferences in Lisbon, Glasgow, and Philadelphia.
- Published four articles in well-ranked journals.
- Worked as a visiting scholar at the University of California for four months.