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углубленные знания в области машинного обучения, интеллектуального анализа данных, рекомендательных систем,
знания математической статистики и теории вероятности,
понимание современных методов и алгоритмов машинного обучения деревья решений, случайный лес, нейронные сети, ближайшие соседи и др.,
опыт разработки моделей машинного обучения на Python 2+ года,
владение SQL, понимание процесса ETL,
английский не ниже Pre-Intermediate чтение и понимание технической документации.

Знания в области машинного обучения, интеллектуального анализа данных
Знания математической статистики и теории вероятности
Опыт разработки ML- моделей на Python не менее 6 мес.
Владение SQL

• 2+ years of proven experience developing data analytics solutions;
• 2+ years of work experience in Python or С++;
• knowledge one of deep learning frameworks (Tensorflow, Pytorch);
• work experience with scikit-learn, scipy, tensorflow (keras) / pytorch, numpy, pandas;
• AWS experience including AWS/S3/Lambda etc;
• strong understanding of machine learning: you should be familiar with the process (data collection, training, evaluation, and making iterative improvements) of building effective learning systems;
• experience developing machine learning-based models;
• practical experience in at least one of the following problems: object detection, segmentation, classification, landmarks;
• familiarity with architectures for deep learning tasks: U-net, ResNet, SSD, Faster R-CNN, LSTM;
• an ability to think outside the box and explore new avenues with natural curiosity and a passion for new subjects and research areas;
• strong statistical analysis skills.

• коммерческий опыт разработки на Python от 3-х лет;
• знание и опыт работы со Spark / PySpark;
• знание SQL;
• опыт работы с Big Data;
• практический опыт эксплуатации Jupyter / Jupyter Hub;
• навыки работы с Hadoop;
• понимание подходов к организации разработки (Ci/CD, DevOps);

3+ years professional experience in Machine Learning;
Industry experience in development and deployment Machine Learning based models to production;
Proficiency with machine learning frameworks like TensorFlow/Bert/Sklearn;
Proficiency with Deep Learning algorithms and packages;
Familiar with CI/CD practices (GitHub actions, Jenkins, AWS Code Pipeline, etc);
English – Intermediate +.

Master’s degree in Computer Science, Engineering or relevant field;
Programming experience in C++, Python and MATLAB;
Familiarity with software development concepts (version control, code review, continuous integration);
Good problem-solving skills;
English - Intermediate.

• MS or higher in the following areas: Statistics and Mathematics;
• At least 3-5 years of professional industry experience, in addition to your academic experience;
• Outstanding quantitative analytical ability;
• In-depth knowledge of Statistics/Probability/Machine Learning;
• General Statistical concepts such as hypothesis testing, estimation, inference;
• Supervised and unsupervised statistical techniques such as regression (linear / logistic), time series analysis, clustering;
• Machine Learning foundations such as bias/variance trade-off, regularization, dimension reduction;
• Real-world experience with popular machine learning algorithms such as Random Forest, Boosting, SVMs;
• Experience with unstructured text data using NLP methods such as Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), Sentiment Models, Word Embeddings, Text Similarity, Entity extraction is a strong plus;
• Strong programming experience in Python and one of the following: Scala/Java, R;
• Understanding of algorithm complexity and performance implications;
• Knowledge of data structures and algorithms;
• Good knowledge of Graph technology, Knowledge Graphs, Graph Databases and Ontologies;
• Experience with SQL;
• Upper-Intermediate level of English.

Master's or PhD degree in a relevant domain (math statistics, math modelling, computer science, etc.);
Deep expertise in Python development. Knowledge of basic data structures and algorithms;
Good knowledge in classical machine learning algorithms, deep learning, modern neural network approaches;
Knowledge of ML frameworks and libraries (more - better): Tensorflow, Keras, Pytorch, Caffe2, scikit-learn, numpy, matplotlib, OpenCV, word2vec.
LP, chatbots development, text content understanding;
Object detection and tracking. -Gesture recognition, scene understanding, movement/action prediction. Semantic segmentation and instance semantic segmentation;
Timeseries analysis: forecasting, clustering and classification;
Data preparation: raw data annotation and labeling, data augmentation and normalization.

• 3+ year experience with Python (statistical and ML packages), advanced knowledge of SQL;
• Understanding theoretical concepts of statistics/probability, machine learning (not just training models);
• Experience in implementing data science to achieve commercial goals;
• Excellent business understanding, problem-solving abilities, and organizational skills;
• Ability to build meaningful visualizations of results.

• 2+ years as Team Leader, working with Agile methodologies;
• 5+ years of industry and research experience in developing machine learning products;
• Excellent written and verbal communication skills, with prior experience explaining assumptions, conclusions, and methodology to both internal and external customers;
• Ability to frame business requirements into Machine Learning problems that you enjoy solving;
• A strong predilection for good software and the processes that make it;
• Mathematical foundation including linear algebra, vector calculus, probability, and statistics. Experience implementing this math effectively in software (e.g. Python, NumPy);
• Strong foundation in machine learning & deep learning concepts including supervised and unsupervised learning, transfer learning, ensembling, classification, regression, clustering, bias & variance, regularization, overfitting & underfitting, Logistic & Linear regressions, Decision Trees, MLP, RNNs;
• Strong foundation in natural language processing concepts including: bag-of-words & TF-IDF, n-grams, word & text embedding, NER, transformers, text classification & similarity;
• Proficiency in Python and PyData stack (NumPy, scipy, pandas, scikit-learn);
• Fluency with popular NLP libraries (spaCy, NLTK, transformers);
• Hands-on experience with Deep Learning frameworks such as PyTorch;
• Experience with SQL;
• Understanding of algorithm complexity and performance implications;
• Knowledge of classical data structures and algorithms;
• Upper-Intermediate level of English.