Position: Lead Data Scientist
Category: Data Science
English level: fluent
Years of experience: 7

Kaggle Performance Tier: Competitions Expert
•	Santander Customer Transaction Prediction. Apr 2019. Bronze medal
•	Google Analytics Customer Revenue Prediction. Feb 2019. Bronze medal
I’ve built my career in a variety of industries and across multiple functional areas, mostly relevant to data science, analytics, etc. And I sincerely enjoy it.

My specialization: predictive modeling (revenue/demand/sales forecasting, scoring, lead-scoring, propensity models, etc.)

Lead contributor to the development of Automated ML Forecasting Platform.
Star Award 2020 for Innovation at work - for outstanding performance and exceptional commitment.
Lead team of data scientists.

Python: numpy, pandas, sklearn, scipy, statsmodels
ML: sklearn: classical supervised (regression, classification) and unsupervised (cluster analysis) learning
ML frameworks: LightGBM, XGBoost, CatBoost 
Deep learning: Keras, TensorFlow:  CNN, LSTM, BiLSTM, GRU, KerasRegressor/Classifier
NLP: nltk, sklearn: tfidf, ngram; NER: nltk, spacy, stanford-ner, polyglot
time series: decomposition (seasonal_decompose, hpfilter), forecasting (sarima, Holt-Winters, tbats, prophet, LSTM, GRU); R: Holtwinters, tbats, hts (forecasting hierarchical time series)
Distributed Computing: PySpark; mmlspark
Cloud computing: Azure HDInsight, Databricks; Amazon SageMaker
App: Flask, FastAPI, docker, Amazon SageMaker