About Me
Data Scientist at NOS, currently leveraging machine learning and data science techniques to generate business value in the telecom field.
My main goal is to work on data science and machine learning applications to solve business problems. I have worked on different real-world problems, including task allocation/scheduling, predictive modelling, image processing and classification, natural language processing, customer churn and customer offer generation.
In my past experience as a researcher I have became skilled in machine learning, data cleansing, normalisation and visualization, and creative thinking. I have successfully worked in optimisation, supervised - classification and regression - and unsupervised learning tasks, across different domains. Over the course of my Ph.D. work, I focused on optimal management of electrical appliances in smart homes, in the presence of uncertain (future) demand and generation.
I have experience with Python and its main data science libraries (Numpy, Scipy, Pandas, Scikit-Learn, TensorFlow, Matplotlib, Seaborn, Statsmodels, Jupyter), R, MATLAB, SQL, Java, JavaScript, C/C++, Bash scripting, Git, DVC and HTML/CSS. I have also worked with different machine learning and data science algorithms, including Gaussian Processes, Artificial Neural Networks, Support Vector Machines, Hierarchical and K-Means Clustering, PCA, LDA. Finally, I have experience with mathematical programming, in particular integer and mixed-integer linear programming, and well-known open source solvers such as SCIP or GLPK.