Position: Machine Learning Engineer/ Data Scientist
Category: Data Science
English level: fluent
Years of experience: 2

Own generator and starlink
Worked with NLP methods of sentiment evaluation of companies mentions in different sources (news/social media).
Worked with churn prediction and analysis for major telecommunication company.
Implemented models of age/gender prediction and deployed to Google Coral Dev Board.
Have managed to built ETL-pipeline using Airflow and AWS services.

Tensorflow/keras for building DenseNet (classification by image) and ResNet transfer learning (classification by sound). gRPC for inference in microservice.

Developed DNN model using keras based on ResNet architecture for multi-label classification.

Kaggle top 8% percent (bronze medal) in competition
I haven't figured out yet which sphere tempts me the most: CV, NLP, or working with tabular data. But, it would be fascinating to apply the ML/DL in the medicine domain, though I'm still want to use my knowledge in different domains.