Position: Data Scientist
Category: Data Science
English level: fluent
Years of experience: 5

1. As a pet - project with a student team we have created an interesting app for the Android with cutting-edge AI. search play.google.com for an "Oahega"
or search for the paper in Google:
"OAHEGA: From Research to Production of Mobile Expression Detection Application"
Links are not allowed here.
2. Get used to have prize-winning places in casual hackathons from time to time. 
3. 50 + courses and certificates in AI, DS, ML, DL (majority) and psychology (minority), mainly Coursera.
4. I have trained growing talent in AI and helped for about 10 people to land their job in IT.
I want to spend 50 % of time leading DS / ML team (along with clients communication) plus use other 50 % for the research and tech tasks.

Short experience Looqme:
1. TSA (target dependent sentiment analysis) API. Automation of data labeling for clients ( brand opinion mining). From idea to production.
Technologies and techniques used: Python, Keras, Tensorflow, nltk, scikit-learn, numpy, Fast API, multiprocessing, AWS cloud training, Glove, MLFlow.
2. Role detection API. Detection of target keyword role in text (object, subject, secondary).
3. Explainable AI for TSA. 
Technologies and techniques used: Python, Keras, Tensorflow, nltk, scikit-learn, numpy, multiprocessing, Glove, lime.
4. Monitoring and infrastructure integration for all AI systems.
Technologies and techniques used: Python, Keras, Tensorflow, nltk, scikit-learn, numpy, Fast API, multiprocessing, AWS cloud training, Glove, MLFlow.

Short experience Vimmi:
1. Designed, developed, deployed and tested (recommendation systems) fully automated ML/AI life circle (pipeline) from data to end user along with A/B tests and monitoring. Recommendations power up content engagement of the users and includes: RNN for personal recommendations, NN (related recommendations), contextual bandits, etc.
Technologies and techniques used: Python, Keras, Tensorflow, w2v, scikit-learn, numpy, LSTM.
2. Designed and created analytical system (ELK stack) based on logs from the streaming servers. System processes for about 2+ TB (near 3 Billion events) of information per day and used by people from diverse positions.
Technologies and techniques used: logstash, elasticsearch, kibana.
I want to work on a single product (to be focused on particular tasks in domain). I think there is always room for improvement, thus quality - my philosophy for work and life.
I don't want to urgently and madly solve not DS/AI tasks.
I am interested in new technologies (for example explainable AI), and want to learn new things all the time.
Also I want to lead a team / or at least to coach few middle-junior people.