● Understanding theoretical concepts of machine learning and neural networks.
● Understanding theoretical concepts of deep learning architectures.
● Understanding how these theoretical concepts could be applied to real-world problems.
● Knowledge and hands-on experience with Python or other relevant programming languages.
● Knowledge and hands-on experience with ML/DL frameworks (PyTorch, TensorFlow etc.).
● Knowledge and hands-on experience with tools for data preprocessing and scraping.
Skills and abilities:
● Strong English verbal and written communication skills.
● Ability to work independently with limited supervision.
● Track record in deep learning, data science, machine learning.
● Relevant levels of theoretical knowledge in data science and machine learning.
● Collecting, transforming, and preprocessing raw data to prepare it for modeling.
● Building machine learning and deep learning models.
● Designing, developing, training, and testing models and algorithms.
● Providing comparative research on different algorithms and models.
● Implementing the model in a form that can be easily used by engineers, documenting its interfaces.
● Delivering the model in a form that can be easily deployable and maintained.
Based on different types of data, candidates can create machine learning / deep learning models. Ability to work in short iteration cycles (up to 2 weeks) from initial research to PoC prototype. Ability to work simultaneously on multiple tasks. Ability to work in a solo or a small team and be responsible for the final results. Lots of educational and self-educational activities.