About

Sahar Alizadeh is a highly experienced Data Scientist with a strong background in Machine Learning, Natural Language Processing, and Deep Learning. Currently working as a Data Scientist at Fandom, she specializes in Advertisement Targeting and Personalization. Sahar has a CS M.S./Ph.D. degree from Oregon State University, completed in 2020. At Fandom, Sahar has designed an ETL pipeline to extract data from S3 AWS and performed data cleaning and manipulation by removing similar contents from train/test using Cosine similarity. She has implemented a TFIDF model to transform pages' contents into embedding and used BertTokenizer (pre-trained) to extract features and make embedding vectors. Sahar has also implemented the Logistic Regression and Random Forest models to predict ads tags and designed an RNN model (Keras, Tensor Flow) for a multi-label prediction model. She has also created an end-to-end train and prediction pipeline using Sklearn. Prior to Fandom, Sahar worked as a Machine Learning Engineer Intern at Twenty Ideas, where she designed a machine learning pipeline for a contextual platform recommendation system. She also worked on Name Entity Recognition (NER) for DBMO.ai, a cloud-based machine learning and artificial intelligence platform designed specifically for the media industry. Sahar implemented a recommendation system to recommend content to users based on user interest. Sahar has also worked as a Research Assistant at Oregon State University, where she applied ML algorithms in a relational database query system for improving interactions between user search queries and search engine results. She designed a user experience platform to evaluate user search queries and compare the results using AmazonTurk. Sahar has a diverse range of technical skills, including Data Science, Research Science, Machine Learning, Recommendation Systems, Random Forest, AWS, Scikit, Keras, RNN, Deep Learning, NLP, ETL, AI, and Word2Vec. With over 10 years of relevant experience, Sahar is a valuable asset to any organization looking to leverage data-driven insights to drive business growth.