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Must-have:
  • 4+ years of experience as ML Engineer with data driven projects. Deep understanding and practical experience with Big Data Stack (Apache or Amazon).
  • Deep knowledge of statistics, probability theory and ML.
  • Practical experience with Python (NumPy, Pandas, Matplotlib, Networkx package, Scikit-Learn).
  • Practical experience with DL modelling using relevant frameworks (TensorFlow,PyTorch, Keras).

Must-have:
  • A degree in Computer Science or Mathematics, or similar field or equivalent experience.
  • Experience with TensorFlow/Python3.
  • Deep learning experience - CNNs, RNNs.
  • Excellent communication skills and ability to explain data-driven insights clearly.
  • Striving for simple solutions to complex problems.

Must-have:
  • Python, Django REST Framework, Mongoengine, Gunicorn.
  • PostgreSQL, MongoDB.
  • Experience with CI / CD.
  • Understanding(and experience) of API documentation.
  • Caching (Redis or RabbitMQ).
  • Celery.

Must-have:
  • React JS, ES6+, nodeJS, SCSS, JSX, Webpack, Gulp, Redux, ExpressJS.
  • Responsive Web Design, Cross-Browser and Cross-Device Compatibility.
  • Understanding of HTTP-protocol.
  • Git.
  • Experience of code coverage with tests.

Must-have:
  • General knowledge of SQL.
  • Advanced proficiency in Python programming language.
  • Strong background in math, linear algebra and statistics. Solid knowledge of algorithms, data structures and algorithmic complexity.
  • Expertise in machine learning. Proven hands-on experience and familiarity with theoretical concepts of common machine learning methods and algorithms.
  • Familiarity with Python numerical operations, data structures, machine learning and visualization libraries: numpy, pandas, sklearn, matplotlib, jupyter.
  • Solid understanding of model tuning and evaluation, ability to implement complete machine learning pipelines.
  • Theoretical knowledge and experience at least in one of the areas: Natural Language Processing, Computer Vision, Deep Learning, Look-alike Modeling, Recommendation Systems, Anomaly Detection.
  • Problem-solving skills, ability to do research tasks.
  • Ability to work with different data types and formats. Data preprocessing, cleaning, feature design and generation skills.
  • Ability to document properly systems functionality, code and methodology
  • Good presentational and visualization skills. Ability to describe and present results clearly, answer questions and communicate issues.