Position: Machine Learning Engineer
Category: Data Science
English level: upper
Years of experience: 4

ML-geek:)
Business areas experience:
- raw text segmentation and analysis;
- keywords extraction and text generalization
- sales funnel optimization;
- advertising and dynamic pricing;
- speech recognition;
- statistics and predictive models;
- any machine learning problem

Algorithms:
- reinforcement learning (MAB, CB, QL, DTS, DQN)
- deep learning (DNN, CNN, RNN, GAN)
- statistical machine learning (Bayesian, Linear, Polynomial, Generative, Instance-based, Ensembles, etc)
- time series (ARIMA, LSTM, CNN, etc)
- natural language processing: 
text cleaning and wrangling, representation learning (BOW, TF-IDF, embedding), similarity measurement and clustering, relation extraction, semantic analysis, sentiment analysis, and keywords extraction (BERT, Rake, Yake, PageRank, etc).

Technical stack:
Programming Languages: Python 2/3, R
ML: Scikit-Learn, XGBoost, LightGBM, TensorFlow, Keras, PyTorch, Ignite, Fastai, Skorch, Xlearn, Gym
NLP: Spacy, NLTK, Gensim, TextBlob
Data analysis: Pandas, SciPy, NumPy, MS Excel
Data Visualisation: Matplotlib, Bokeh, Seaborn, Plotly, D3.js
AutoML: PyCaret, MLflow, Auto-Sklearn
DBMS: SQLite, PostgreSQL, BigQuery, MySQL, MongoDB
Web Scraping: BeautifulSoup4, Scrapy, Splash, Selenium
Big Data: Hadoop, Spark, MLlib
Web: Flask, Aiohttp, Django-rest
Cloud: AWS, GCP 
Other: Linux, Git, Docker, ElasticSearch, Cython
Professional interests: natural language processing, recommendation systems, bioinformatics, and medicine. But also interested in any area where ML could be implemented. 
Expected an exciting project and a friendly team:)