— 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. clustering algorithms, dimensionality reduction (PCA, t-SNE). A good foundation in basic statistics and linear algebra;
— Strong Python knowledge;
— 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, XLM-RoBERTa
— Strong practical experience with Deep Learning frameworks like PyTorch, MXNet, TensorFlow, or Keras;
— Intermediate level of English.
— 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 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.
— Competitive compensation and benefits
— Flexible working schedule
— Remote work or work in one of our development offices
— Covered rest period (20 business days+ 5 days-off)
— Professional growth: a variety of projects, regular technical events, mentorship.
— Free English classes (we have an amazing English teaching team)
— Speaking-club with a native English speaker
— Truly friendly atmosphere and teambuilding