Jan `18 – Nov `18
Zpoken Speech Recognition API
Sr. Natural Language Engineer
• Prepared the model data and built machine learning algorithms using Python Pandas, scikit learn, numPy, keras etc. libraries using Anaconda Jupyter & Programming Linear, Logistic Regressions, KNN,K-Means Clustering, Sentiment/Text Analytics, NLP, Natve Bayes, Time Series forecasting using lm, glm, Arima, Apriori, Forecast. • Working on Information extraction from different kinds of text documents using NLP, text mining and regular expressions.
Zpoken STT (speech-to-text) engine – is a leading speech recognition software used for audio transcription, analytics, and voice-enabled applications. STT includes 5 different languages (English, Russian, Ukrainian, German and Chinese) that were developed from scratch and have accuracy more than 97%.
Technologies Stack:C++ MongoDB PostgreSQL Python Data Modeling Docker TensorFlow Caffe scikit-learn
Business & Productivity, Internet & Telecom, Data Science & Machine Learning, Big Data
AI- and NLP-driven language learning solution, Speech-to-text engine and handwritten chars generation
Sep `17 – Dec `17
Sr. Data Scientist
• Implementing end-to-end systems for Data Analytics, Data Automation and integrated with custom visualization tools using R, Hadoop and MongoDB, Cassandra. • Performing machine learning algorithms in R and used Spark for test data analytics using MLLib and Analyzed the performance to identify bottlenecks. • Working with Machine Learning Algorithms such as Decision Trees, Random Forest, Gradient Boosting, Support Vector Machines, K Mean Clustering, Naive Bayes
Analytics of the Healthcare databases
Technologies Stack:Python R Spark JSON R-Hadoop MLlib scikit-learn
Healthcare & Medicine
A complex Data Science project
Mar `17 – Aug `17
AI Trading Bot
Machine Learning Engineer
• Developing Predictive models, Machine learning (Supervised and non-Supervised) using R for Machine Motor. • Creating analytical models using analytics algorithms like regression, decision trees, clustering, text mining etc. and leveraging tools like R, Tableau etc. to deliver actionable insights and recommendations.
Trading bot uses a multitude of indicators and points of analysis to give users predictive buy/sell signals based on an amalgamation of historical and live data to fully analyze and predict the cryptocurrency market. It is capable of understanding the difference between a bull and bear market, how to adjust the meaning behind indicators based on the market state, understands the theory behind technical analysis, such as when RSI is reversed.
Technologies Stack:PostgreSQL Python R RabbitMQ Docker TensorFlow Caffe
Banking & Finance, Blockchain & Cryptocurrency
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”
Master’s degree in Applied System Analysis
Nvidia Developer Program
Machine Learning Developer
Nvidia Deep Learning Institute
Machine Learning Developer