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Must-have:

— Python3 + classic ML/CV stack (numpy, pandas, sklearn, opencv)
— Deep Learning frameworks: PyTorch/TensorFlow (PyTorch is preferred)
— theoretical concepts of Machine Learning and Deep Learning.
— 3+ years of DS/ML experience (at least 2 years of CV experience)
— practical experience in at least two of the following problems: image classification, object detection, segmentation, OCR, metrics learning
— experience in deploying ML models to production
— good English for reading SOTA articles and communicating with foreign colleagues
— goal-oriented mindset


Must-have:

Knowledgeable with 3+ years of relevant industry experience and advanced degree in machine learning, computer science, statistics, biostatistics, mathematics, or related quantitative field
Proven track record of shipping machine learning-powered algorithm products at B2C-like scale as well as working with cross-functional teams in an agile-like environment
Your grasp of machine learning fundamentals and ability to design intuitive, working ML solutions in response to complex business problems
You have a strength in the “design and prototype” part of the ML development pipeline, beginning with pulling datasets from SQL and ending with serializing ML models and assisting engineers to product-ionize model retraining and model serving systems
When it comes to communicating, you have no problem with ML/algorithm designs clearly to cross-functional team members, especially engineers and product managers
You are well versed in SQL data warehouses such as Redshift and Snowflake, have worked on current ML tools such as TensorFlow, PyTorch, and Python, and feel comfortable with recommender systems or natural language processing
To take it one step further, you are effective at translating and blending traditionally distinct ML concepts such as recommender systems, NLP, regression, and classification into a common framework such as TensorFlow
Enlish level — Upper Intermediate


Must-have:

2+ years of commercial experience in Data Scientist;
English level Upper-Intermediate;
Finance/Banking domain or similar approaches (behavioral models, scoring/recommendation systems);
Investigate, analyze, and interpret large data sets;
Experience with Content-based information retrieval;
Design, develop and implement graphs, metrics, dashboards, and reports to visualize data and support decision making;
Taking full ownership on building data products from design to implementation and deployment;
Knowledge of DS libraries and AI/ML techniques;
Experience in SQL;
Experience in Microsoft stack (Azure, Data Lake, PowerBI etc.).


Must-have:

Высшее образование или студент 4+ курса по направлению математика, статистика, информатика;
Умение работать с базами данных, знания SQL;
Знание в области построения математических моделей (лог регрессия, деревья решений, нейронные сети...);
Опыт работы с R, python, sas или другими инструментами построения моделей.


Must-have:
  • Regularly creates, deploys, and uses machine learning algorithms to solve business objectives
  • Familiar with translating business objectives into (predictive) models
  • Familiar with objective function minimization and product-driven model development
  • 4+ years coding experience in Python with strong capabilities in major frameworks such as Pandas, sklearn
  • Experience building data science pipelines to ingest, transform and extract value from data
  • Strong capability describing data distributions using statistical methods
  • Strong knowledge of SQL
  • Accomplished at combining data from multiple sources by grouping/aggregation to produce desired datasets
  • Expert at visualizing/presenting data for stakeholders
  • Strong communication skills especially describing technical work during weekly progress meetings

Must-have:

C++/Python
Apache Spark
Data Engineering,
SQL


Must-have:

— Practical experience with ML models in production (2+ years);
— Python 3.х, software engineering skills (able to produce well-structured production-level projects, not only notebook scripts);
-- Experience with ML frameworks and libraries: numpy, pandas, nltk, PyTorch, scikit-learn, spaCy, gensim LSTM, transformers (BERT, GPT);
— Good knowledge of ML theory and practice — pros & cons of different model types, validation, metrics, etc.;
-- Understanding theoretical concepts of NLP (language modeling, text classification, sequence classification, question answering, etc);


Must-have:

• Proven 2+ years experience as a Machine Learning Engineer
• Solid understanding of machine learning fundamentals
• Deep knowledge of math, probability, statistics and algorithms
• Understanding of data structures, data modeling and software architecture
• Expertise in visualizing and manipulating big datasets
• Proficiency with a TensorFlow (Keras) or PyTorch
• Ability to write robust code in Python
• Strong proficiency with basic libraries for machine learning (scikit-learn, matplotib, numpy, pandas)


Must-have:

• profound practical experience in data science
• experience in maintainance of model-based processes
• strong background in statistics and machine learning (or related fields as e.g. information retrieval, analytics, reinforcement learning, etc.)
• good knowledge of Python
• ablility to solve problems using data
• good communication skills
• fluently in English

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