Rahul Kulkarni is a Data Intelligence/Evangelist with over seven years of experience in developing intelligent Data Science applications and helping businesses take strategic decisions. He has led innovation and developments across different verticals and is an expert in Python, R, SAS, Scala, Shell Scripting, SQL/PLSQL, Apache Spark, Hadoop Eco-System, Tableau, NLTK, TensorFlow, CNTK, Windows Application Packaging, Web-services, DevOps, and Microservices.

Mr. Kulkarni is a statistical modeling expert with expertise in data munging, decision science, machine learning, graph analysis, text mining, and optimization, web scraping, mix models, Monte Carlo simulation, multinomial logistic regression, neural networks, random forest, random walk, restricted Boltzmann machine, and Smith-Waterman alignment, among others. He has experience with SVD, SVM, one-class SVM, Spectral clustering, TF-IDF scoring, Vector space model, Conditional Random fields, Gaussian process, Gradient boosted trees, Elasticsearch, Hidden Markov models, Hierarchical clustering, Information retrieval algorithms, K-Means, K Nearest Neighbors, Kalman filter, Latent Dirichlet allocation, Latent semantic indexing, Linear regression, Logistic regression, Markov process, Maximum entropy Markov models, Association rules, Linear programming, Quadratic programming, and Genetic algorithms.

Mr. Kulkarni has secondary skills in Oracle DBA, Linux Administration, Batch Scripting, Build Tools(Jenkins & Docker), Application Packaging, Excel Macro's, and has an MS in Computer Science from the University of Illinois at Urbana-Champaign. He has worked as a Data Scientist at Microsoft, Data Scientist-Consultant at Microsoft, Algorithm Developer at Unilectric, Sr. Data Scientist at Mu Sigma Inc., Sr. Analytics Developer at Mu Sigma Inc., Program Analyst at Cognizant, and has had a Software Developer Internship at UPTEC IDEA LABS.