Karla V.

Data Scientist from the University of Edinburgh specialized in Machine Learning, Data Analysis, Business Intelligence and Statistics. Five years of professional experience working in Data, Analytics and Marketing areas. I have developed consulting projects with Lloyds Bank to predict network attacks using Machine Learning techniques such as Feature Engineering; and a project with Space Intelligence to fill the gaps in satellite images using XGBoost models. Not to mention some other activities to predict and classify using supervised and unsupervised learning with Python and R. I am currently developing an End-to-end Data Science Project using optimization with Python to increase the conversion rate, and optimize their processes. I have experience using the following software: AWS. Quick Sight, S3, Transcribe, Comprehend. • Python. TensorFlow, Scikit-learn, Pandas, Seaborn, Matplotlib, Numpy, XGBoost, Keras, PySpark, Scipy. (Jupyter, VSC, Colab). • R. JAGS, INLA. • Google Cloud Platform. BigQuery, Looker, Dataproc, Pub/Sub, Dataflow, Data Catalog, Dataprep. • SAS, Minitab, SQL, Salesforce, Bizagi, Neo4j, Cypher, Tableau and Power BI. To finish I would also like to highlight that my constant work and willingness to become a better professional helped me to achieve a fully funded scholarship from the University of Edinburgh and to be a winner of the AWS Hackathon BBVA in 2020, being the first generation to achieve it.