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Must Have:

Experience in Deep Learning Libraries such as TensorFlow, Keras, PyTorch Text-wrangling and pre-processing skills such as document parsing and cleanup, vectorization, tokenization, language modeling, phrase detection. Experience in implementing Word2Vec and Neural Word Embeddings using Gensim or Glove. Hands on experience in Natural Language Processing tools (e.g., Spacy, NLTK, OpenNLP, or similar) Clustering and Classification of Text, Question Answering, Text Mining, Information Retrieval, Distributional Semantics, Knowledge Engineering, Search Rank and Recommendation. Experience in implementing sequence models using RNN, LSTM. Well versed in querying and analyzing data using programming languages such as Python, R, SQL, etc. Experience in leading ML Libraries including Python (scikit-learn, genism, etc.),  Visualization using Python (Matplotlib, Seaborn, bokeh, etc.) 

Good to Have:

Basic to Intermediate exposure in analyzing unstructured data like Text (NLP) & Images (Computer Vision). Exposure to Deep Learning Libraries such as TensorFlow, Keras, PyTorch Hands on experience in Machine learning techniques for addressing a variety of problems (e.g. consumer segmentation, revenue forecasting etc.) Excellent understanding of supervised and unsupervised machine learning techniques for regression, classification and clustering tasks

Minimum years of experience*: 12+