About

Nitesh Kansal is an accomplished Applied Scientist at Amazon with over 10 years of experience in the field of Machine Learning and Statistics. He has a diverse skill set that includes Natural Language Processing, Boosting and Bagging Models, Regression, Support Vector Machines, Bayes Nets, Deep Learning, Clustering, Time Series Modeling, Topic Mining, Anomaly Detection, Recommender Systems, and Probabilistic Graphical Models. Nitesh is proficient in a variety of programming languages such as Python (ML, Pandas, Deep Learning, Visualization), C, C++, Spark (ML, Pipelines), Java, SaS, Matlab, Flask (basics), and SQL. He is also comfortable working with technologies such as MySQL, PostgreSQL, Hadoop, HDFS, Amazon Redshift, AWS Lambda, and DynamoDB. Prior to his current role at Amazon, Nitesh has worked as a Data Scientist at Directi and Dream11, where he was responsible for identifying and capturing useful customer data, designing and implementing scalable solutions for problems like League Generation, Product Recommendation, Fraud Detection, Churn Prediction, and more. He has used techniques such as Xgboost, Bayesian Optimization, Cosine Clustering, Spectral Clustering, Naive Bayes, SVM, and Regression in his work. Nitesh holds a Dual Degree in Chemical Engineering from the Indian Institute of Technology, Delhi. He is a Research Scientist, an expert in ML, NLP, Deep Learning, SQL, AWS, and a skilled Data Scientist. He has experience working with Python, Spark, HDFS, Pandas, Hadoop, MySQL, Flask, Search, Java, test, Tensorflow, Pytorch, Naive Bayes, SVM, XGBoost, AI, Scikit, dotNET, Integration, Mobile, Software Engineer, and Apache Hive. Nitesh's primary area of interest and expertise is Machine Learning and Statistics, and his secondary area of interest is Algorithms and Data Structures. He is a highly skilled professional who is passionate about using his expertise to solve complex problems and drive innovation in the field of Machine Learning.