Full Stack Developer @ Kochav Yair, Central
Principal of Artificial Intelligence and Data Science Delivery at Capgemini Invent, North America
Warren, New Jersey
Strategic thinker, creative problem solver, technical leader, project manager, and a strong hands-on data scientist with 15+ years of experience in AI, data science, machine learning, statistics, large databases, distributed-fault-tolerant computing, and large digitization projects. Possesses a wide range of problem solving, interpersonal, technical, and communication skills. Pays attention to details, excellent team player, and goal oriented. Driven by results and business impact.
• Clustering, classification – SVM, KNN, logistic, Bayesian, decision trees (random, boosted), model explanation (LIME).
• Artificial neural networks – auto-coders, word embedding, CNN – Keras, Tensor Flow.
• Dimensionality reduction – SVD, NMF, Self-Organizing Maps, identification of latent attributes.
• KNN techniques and vector space similarity modeling – for recommendation engines and product affinity.
• NLP: bag-of-words, topic analysis (LSA, pLSA, LDA), word embedding (GloVe, Word2Vec, Gensim), distance metrics, SOM to create “near-equivalent” words, Explicit Semantic Analysis.
• Document similarity analysis and clustering: data-driven determination of number of clusters, extraction of skill/talent profile from resumes, extraction of culture profiles from annual performance review text, matching documents to customer requests, intent/topic detection in forum text, and spam detection.
• Ordinary and logistic regression models, GLM. Piecewise and localized regression.
• PCA, factor analysis, kernel smoothing.
• Variable selection, hypothesis testing, inferencing.
• Large databases, data warehousing,
• Data integration, preprocessing, and harmonization.
• Mathematical algorithms.
• Distributed software (Hadoop, map-reduce). High-performance, real-time software.
• Define digital strategies to support data-driven decision making.
• Create advanced analytics, data science, and AI facilities to achieve transformational objectives.
• Identify gaps in data landscape that would hinder achieving digitization objectives.
• Business sectors: manufacturing, consumer goods, telecommunications, retail, and financial services.