Experience must have included Use of programming skills and experience building systems in Python; Performing data exploration, visualization, and data cleaning; Applying machine learning algorithms to real data sets; Use of a wide variety of supervised and unsupervised machine learning techniques including random forests, logistical regression, deep learning LSTMs and CNNs, Bayesian techniques, and graphical models; Working with machine learning and NLP tools and libraries including NumPy, SciPy, Matplotlib, ScikitLearn, NLTK, SpaCy, TensorFlow, Keras and ElasticSearch; Building out APls designed to work at scale; Building out working prototypes quickly; and Taking ambiguous problems through to clear problem statements and delivered solutions.
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