Position: Data Scientist
Category: Data Science
English level: intermediate
Years of experience: 2

- Analyzing potential segments and forecasting the future demand of new customers in the bank
- Built customer churn model, which helped reduce client churn by ~20% with LogisticRegression, RandomForestClassifier, XGBClassifier, Pandas, Numpy, Plotly.
- Developed a recommendation system for assigning relevant personal offers customers in the banks with Alternating Least Squares, K-nearest neighbor using Implicit
- Research and implement NLP methods to extract relevant information from reviews in the webspace
- Trained and deployed text classifiers with Word Embeddings, LSTM, BERT using TensorFlow
- Develop and implement models using ML and NLP to analyze the business models of clients.
- Built Sentiment Classifier using Naive Bayes and Multiclass Logistic Regression to classify company reviews as +1, 0, -1 and implemented metrics to analyze sentiment distribution over different demographics.
- Built a time series model with 5000 data with a prediction for 3 years on the base of Python using Scikit-learn, Pandas, Numpy, Plotly.
Data scientist with 2+ years of experience in analyzing large data sets, implementing data preprocessing pipelines, and deploying machine learning models. The main domain of my work is IT, Marketing, Retail, Logistics, Bank. A confident user of Various Machine Learning algorithms: Regressions, Classifications, Clusterization, Time Series Forecasting, NLP, Image Analysis, Deep Learning, etc. A data-driven developer with highly adaptive skills in analyzing and investigating. Have a strong marketing background, problem-solving skills, and the ability to perform complicated tasks.