Do you want to boost your practical skills in Machine Learning? Do you want to work on complex problems where amount of data makes it impossible for humans to solve it manually? The position we offer is exposed to full lifecycle of Machine Learning research and development: business evaluation, research, literature review, design, ETL, model design and evaluation and finally model productization. The problems were solving arent to be seen in the wild in the industry.
The work is split into independent business-oriented experiments that leverage Machine Learning in order to streamline internal processes and automate tedious work done manually to-date. Input for these experiments is often unstructured and data rich. It covers both textual data and proprietary binary formats that need to be appropriately handled. Amount of data varies per-experiment and spans from megabytes to terabytes.
- Jupyter notebooks (hub + lab, remote)
- Remote Linux servers
- pandas, numpy, scipy, TensorFlow, and lot of other FOSS and proprietary libraries
- Argo workflow
- ELK (ElasticSearch, Logstash, Kibana)
- SQL, Redis, Cassandra
- M.Sc. or higher degree in Data Science, Computer Science, Mathematics, or similar field of study
- Good Python knowledge
- Knowledge of Python scientific libraries: pandas, numpy, scipy
- Ability to work with different data formats: CSV, JSON, SQL, Numpy arrays, pandas DataFrame
- Business orientation
- Problem solving skills
- Intellectual curiosity
- Communicative English
- Professional experience in Machine Learning is a plus
- Experience of solving Kaggle.com problems is a plus
- Productization of ml based solution is a plus
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