Data Scientist (offline)
—Strong knowledge and practical experience in Natural Language Processing (NLP) area, i.e. tf-idf, word embedding, word2vec, Transformers, BERT
—Good knowledge in machine learning i.e. random forest, xgboost, clustering algorithms, dimensionality reduction(PCA, t-SNE). A good foundation in basic statistics and linear algebra.
—Strong knowledge of Python;
—Comprehensive knowledge of the Python data analyses ecosystem (Pandas, Numpy, Scikit-learn, etc.);
—At least minor experience with python visualization tools(matplotlib/seaborn, Plotly) —Strong practical experience with NLP frameworks: fastText, spaCy
—Experience with following neural network architectures: LSTM, GRU and other RNN-based
—Strong practical experience with Deep Learning frameworks like PyTorch, MXNet, Tensorflow, or Keras.
—Upper-intermediate level of English mandatory
Would be a plus:
—Experience with R, C++
—Familiarity with time-series predictive/anomaly detection analyses, natural language processing, signal processing
—Understanding SOTA approaches for machine learning problems like unsupervised/semisupervised learning.
—Experience with the following DL frameworks: DLib, Darknet, Theano.
—Awareness of the CRISP-DM process model
—Experience with continuous integration and release management tools, preferably within the AWS platform.
—Hands-on Experience with the common architecture of MLOps system by the means of Hadoop, Docker, Kubernetes, cloud services and experience with managing production ML lifecycle
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Job unpublished on
30 August 2020
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