Sreekanth Mallikarjun

Background

About Sreekanth Mallikarjun

• Ask Valuable Questions • Design Reliable Processes • Achieve Accurate Results • Data Science professional excelling in both corporate and academic environments with proven track record. Respected by peers and senior leaders for my commitment to achievement innovation and driving results with available resources. • I enjoy identifying and solving business problems with a results-focused strategy to enhance organizational efficiency and achieve a competitive advantage. • Adaptable to a variety of fields. HIGHLIGHTS: • Data Science Modeling and Statistical Analysis • Machine Learning • Natural Language Processing • Quantitative Methods • Business Intelligence • Applied Strategy • Operations and Process Development • Complex Problem Resolution • Cross-Functional Know-How • Competitive Intelligence • Performance Analysis TOOLS: Programming: R Python Database: SQL MongoDB ElasticSearch TECHNIQUES: Machine Learning: •Supervised: Regression Logistic GLM NN SVM Trees Bagging Forests Boosting Naïve Bayes LDA Lasso Ridge •Unsupervised: Dimensionality Reduction Clustering Anomaly Detection Factor Analysis Recommender Systems •Reinforcement: Markov Decision Processes Game Theory Q-Learning •Deep Learning: LSTM RNN CNN Natural Language Processing: Classification Information Extraction Text Mining TF-IDF Topic Modelling Sentiment Analysis -- Viterbi Naïve Bayes Maxent classifiers n-grams Dependency Parsing Word2Vec Operations Research: Data Envelopment Analysis Optimization Monte Carlo and Discrete Simulation Forecasting Time-Series Multi-criteria Decision Making Waiting Line Models Distribution and Network Modeling Data Mining: Pattern Discovery Cluster Analysis Record Linkage Statistics: Frequency Bayesian ANOVA Estimation Parametric and Non-Parametric Hypothesis Testing