• 18+ years integrating and managing Machine Learning / Deep Learning / MLIP Optimization products and solutions across Retail, CPG, E-commerce, and Payment Cards industries• 12+ years leading cross-functional teams of Data Scientists, ML Engineers, Software Engineers, and Managers with focus on performance, talent development and mentoring.• Skilled at partnering with executive leadership, business partners and engineering teams.• Passionate ML engineer and software developer with quick ability to learn new technologies.• MBA - Marketing, Finance & Business Analytics from global top 20 business school, B.Tech. -Computer Engineering from India's tier 1 engineering college (NIT).Core Competencies:Crafting vision & roadmap, creating and scaling ML/AI products, team building, problem-solvingProjects Executed:Customer acquisition, retention / loyalty, promotion optimization, fraud and credit risk optimization, marketing mix modeling, multi-channel attribution, purchase propensity, customer lifetime value, A/B testing, recommendations / collaborative filtering, feature store, data pipelines, R to pythonProgramming Languages:Python – pandas, scikit-learn, numpy, scipy, matplotlib, seaborn, pyspark, tensorflow, pytorch, LightGBM, beautiful soup, threading, multiprocessing, SQLAlchemy, PyOD; R – dplyr, tidyr, datatable, ggplot, lattice, carat, nnet, XGBoost, randomForest, sqldf; Bash – shell scripting, C, C++, Java Script, Advanced SQL (MySQL, DB2, Oracle, Teradata)ML Algorithms:Linear Regression (Regularization Ridge, Lasso, Elastic Net), GLM, Logistic Regression, Support Vector Machine, Random Forest, Extreme Gradient Boosting (XGBoost), LightGBM, CatBoost, Neural Networks - ANN, CNN, VGG, AdamNet, ResNet, RNN, LSTM, GRU, Generative AI, Encoders, Decoders, Transformers. Seq2seq, LLMs, Llama 2, GPT, Autoencoders, GAN, Transfer Learning, K-means, Hierarchical clustering, DBScan, Isolation Forests, Bagging, Boosting, Stacking, Bootstrapping, Probabilistic / Markov Models, Bayesian Networks, MCMC, Linear Discriminant Analysis (LDA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Linear / Non-Linear Mathematical Optimization, Natural Language Processing (NLP), Word Embeddings (TFIDF, Word2Vec, Fasttext - CBOW & Skipgram), Tokenization, Reinforcement Learning, Matrix FactorizationTools:AWS, Kubernetes, Kubeflow, Docker, Kafka, Spark, Gurobi, CPLEX, Git, Cron, Vim, sed, awk, Tableau, Jira, ConfluenceAPIs / Sources:Hugging Face, Adobe, Marketing Cloud, Ensighten, Experian, Threatmetrix, Kount, MaxMind, Whitepages, Lexis Nexis, Telesign