About

Moid Hassan is a highly experienced Data Scientist with over 5 years of experience in the field of data science and analytics. He is currently working at Microsoft as a Data Scientist II, where he manages the entire lifecycle of data science projects, from designing project pipelines to building and evaluating machine learning models. Moid has a deep understanding of machine learning algorithms and has hands-on experience in building production-ready scalable machine learning systems from scratch. He has mentored interns and Data Scientists for the last 1 year, showcasing his leadership skills. Moid has a strong technical background and is proficient in using Pyspark, Python, Tensorflow, SQL, Hive, and PIG. He has worked on various categories of predictive models, including qualification, credit card propensity, uplift, and interest-based models, for prospect acquisition for proprietary cards via digital channels using GBM and XGBoost. His work has led to a 10%+ improvement in approval rates in Media channel, which was measured by A/B testing. Moid has also developed and automated the Media Mix Modeling (MMM) methodology, which measures the impact of various marketing activities on different outcomes like conversions, site visits, clicks, etc. It is further used to optimize the advertising mix with respect to these different outcomes. Moid has worked with various internal and external data sources, including bureau, demographic, spend, firmographic, mobile, and social data, to enhance feature engineering and capture intelligence for use in acquisition modeling. He has a Bachelor of Engineering (B.Eng.) degree in Information Technology from Panjab University. Moid's tech stack includes Data Science, Research Scientist, ML, AI, Python, SQL, XGBoost, Mobile, NLP, Tensorflow, Hadoop, Apache Hive, Deep Learning, HDFS, Hyperparameter, Spark, Scikit, MapReduce, Big Data, Pandas, Search, Backend, and Web. With 6.86 years of relevant experience, Moid is a highly skilled and proficient Data Scientist with a proven track record of delivering successful data science projects.