About

Anand Kumar is a certified Machine Learning and Data Analytics professional with over 10 years of industry experience and 3 years of experience in ML projects. He is currently working as an AVP-Data Science at Wells Fargo, where he specializes in Cyber Security, Machine Learning, Deep Learning, and NLP. Anand has experience in a wide range of analytics fields such as Artificial Intelligence, Natural Language Processing, Time Series Analysis, Market Basket Analysis, Anomaly Detection, Recommendation System, Descriptive & Inferential Statistics, and more. He is well-versed in Python, including Spyder and Jupyter, and has experience working with databases such as ORACLE. He is also familiar with Big Data tools like Hadoop and Pyspark and has worked with APIs such as Flask. Anand has experience in model deployment on cloud platforms like AWS (Sagemaker, EC-2, S3) and Heroku. He has worked with various companies in the past, including Arisglobal as a Technical Lead and Metricstream as a Senior Member of Technical Staff. At Metricstream, he worked on data pre-processing and cleaning, feature engineering, data imputation techniques, and visualization techniques using Python. He also performed necessary tasks such as outlier treatment, scaling, and multicollinearity issue using VIF. He modeled and predicted using Random Forest after trying out other algorithms such as Logistic, SVM, and Decision Tree. He performed data-driven analysis using Python on issue and risk data to get insight, which helped mitigate risk and provide GRC-based solutions. Anand holds a PG Diploma in Data Science & Machine Learning from the International Institute of Information Technology – Bangalore, a PGDS from Upgrad, and a Master of Computer Applications (MCA) in Computer Science from Cochin University. Overall, Anand is a highly skilled Data Scientist with a strong background in ML, NLP, Deep Learning, and Cyber Security. He has relevant experience of 8.52 years and has worked with a variety of analytics tools, databases, and big data tools.