— Strong knowledge in machine learning fundamentals i.e. linear models, decision trees, naïve bayes, clustering algorithms, dimensionality reduction (PCA, t-SNE) and a good grasp of the strengths and weaknesses of specific approaches. Good foundation in basic statistics and linear algebra;
— Strong Python knowledge;
— Strong practical experience with Deep Learning frameworks like PyTorch, Tensorflow or Keras;
— At least minor experience with python visualization tools(matplotlib/seaborn, Plotly);
— Comprehensive knowledge of the Python data analyses ecosystem (Pandas, Numpy, Scikit-learn etc.);
— At least minor experience with natural language processing;
— Experience working in Linux, Unix environment.
As a plus
— Experience with Tranformers architecture (GPT-2, BERT, etc);
— Understanding SOTA approaches for machine learning problems in NLP domain;
— Experience of work with LSTM models.
— 40-hour workweek with flexible working hours (start from 8 to 11 am);
— Annual paid vacations (18 working days);
— Paid days off on National Holidays;
— Paid sick leaves (10 working days);
— Possibility to work 2 days per month remotely;
— English classes (for different levels of language skills; etc.);
— External and internal professional trainings and conferences;
— Continuous learning opportunities and personal development plan;
— Tax compensation (Social & Single Social Payment) and accounting support;
— Engaging working process with challenging tasks in a professional environment;
— Open-minded, collaboration-friendly, invention-driven team;
— Corporate and team events.
— Participate in a massive NLP project from the early prototype stage;
— Research and implement approaches to improve project result quality and optimize speed;
— Move from prototype to production, ensuring scalability of models training and models inferring.
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