Position: Data Scientist
Category: Data Science
English level: fluent
Years of experience: 6

1) I have created a speech to text model in half a year for Azerbaijani and now it is the most accurate model. It gets 10.8% WER, where our opponent gets 28.9% WER and Google gets 56.9% WER.

2) I have built the first automatic media monitoring system for Azerbaijani, where I have applied, Named Entity Recognition, Sentiment Analysis, Text Classification, Keyword Extraction and Summarization.
- Best Azerbaijani speech to text model:
I have built the speech to text model for Azerbaijani language using dataset that we used crowdsourcing to gather.
Technologies used: kaldi, srilm, python, bash, pytorch, numpy, pandas

-  Azerbaijani text to speech model:
I have built a text to speech model for Azerbaijani language using relatively clean utterances of two speakers. I used tacotron 2 and parallel wavegan models.
Tools used: python, pytorch, numpy, pandas,

- Media Monitoring project:
Wrote 200 crawlers to continuously grab news from 200 different news channels in Azerbaijan with a junior developer. Also applied generic crawlers to grab the news inside even if the html in the page changes.
Applied Similarity Detection on those news to get the unique news.
Applied Sentiment Analysis to classify those news as positive or negative.
Applied Text Classification to classify those news to different categories (Sport, Economics etc.)
Applied Named Entity Recognition to detect entities passing in the news to match them with the positive and negative news, so we can say that the name of this person or organisation is passing on negative news x% of the time.
 Tools used: python, scikit, numpy, tensorflow, keras

Similarity detection, Named Entity Recognition, Sentiment Analysis, Text Classification. Tools used: python, scikit, numpy
I want a place where I can continuously improve myself and work on different projects in Natural Language Processing, Speech Recognition, Computer Vision. Also I want a fair amount of payment.