Position: Machine Learning Engineer
Category: Data Science
English level: fluent
Years of experience: 2


TB Detection from Chest X-ray: | OpenCV, Django, TensorFlow, SQL
• Used a pre-trained U-Net model with 97% dice coefficient and MobileNetV2 with 98% for lung area segmentation
and Lung disease classification.

Customer Clustering: | Pandas, Sci-kit-learn, Numpy, Seaborn, Matplotlib, Git
• Used data analysis and clustering to identify consumer experience, engagement, and satisfaction and found that
71% of the customers were satisfied with the service.

Creative Optimization: | TensorFlow, OpenCV, Sci-kit-Learn, DVC, MLFLOW
• Computer vision algorithms to extract features from creative assets and predict the related KPI performances.

Entity Extraction and News Artifact Classification: | Cohere, Python, Flask, Streamlit, Docker, MLFLOW, DVC
• Applied Large Language Model for extracting entities from job descriptions and classifying news given only a few
examples of human scores