About

Sarath Haridas is a highly skilled Data Scientist with over 7 years of experience in the field. He is currently working as a Data Scientist at Meta, where he is responsible for developing and implementing machine learning models to solve complex business problems. Prior to this, he worked as a Principal Associate Data Scientist at Capital One, where he identified growth opportunities worth $39M in NPV by creating a causal inference pipeline for customer management using daily refresh data. He also established himself as a subject matter expert for daily refresh data and collaborated with business teams to design early credit intervention strategies for credit limit decrease programs. Sarath's expertise lies in using tools such as Python (NumPy, Pandas, Scikit-Learn), R, SQL, SAS Enterprise Guide, VBA, and MS Office to analyze large datasets. He is also proficient in big data platforms such as Hadoop, Hive, Spark, Pig, and AWS. Sarath has extensive experience in supervised machine learning techniques such as linear regression, logistic regression, decision tree, K-nearest neighbor, support vector machine, random forest, boosting methods, and neural networks (CNN, RNN, LSTM). He is also well-versed in unsupervised machine learning techniques such as association rule mining, latent Dirichlet allocation, clustering techniques like k-means, k-prototype, DBSCAN, Gaussian mixture model, and hierarchical clustering. Sarath holds a Master of Science degree in Business Analytics from the University of Minnesota and a Bachelor of Technology degree in Mechanical Engineering from NIT Tiruchirappalli. He is a highly motivated and results-oriented professional with a proven track record of delivering high-quality work within tight deadlines. Sarath's technical skills, combined with his business acumen, make him a valuable asset to any organization.