Position: Data Scientist, ML Developer
Category: Data Science
English level: intermediate
Years of experience: 3

- in a year changed the field of activity from the head of the sales department to the Data Scientist
    - wrote a parser that collected real estate data  and processed it for further storage and model training
    - Reduced mdape error from 10 to 5 percent when modeling property value forecasts.
    - within the framework of the project on the classification of real estate objects by photographs, an accuracy level of 97% was achieved
    - as part of a speech recognition project, I wrote a module for recognizing and converting voice into auditory, and text into text features for further training the model
    - Ph.D. Candidate in Сomputer Science, Odessa National Maritime University, Odessa, Ukraine
- For 2 years I have been involved in a startup that predicts the price of rent, sale and mortgage of real estate objects.

 - During this period, I have developed and implemented more than 40 machine learning models based on Random Forest, XGBoost, LightGBM and others, including on AWS with an error level of up to 5% (incl POI, text description to featrures)

 - I have experience in deploying machine learning models based on the Python, including through the front-end (HTML, CSS) and through the API

 - There is experience in data parsing (BeautifulSoup) and the use of NLP in converting object descriptions into their features (BERT, GloVe, TD-IDF), skills in working with SQL databases

 - Working with geospatial characteristics, clustering (DBSCAN) and detection of anomalous data

 - Time Series based forecasting the dynamics of real estate prices with macroeconomic factors (Linear and Polynomial Regressions, VectorAutoregression, SARIMA, FB Prophet with exogenous factors, interpolation, savgol filter)

 - NLP based classification of Customers (classification by description with vectorization (TF-IDF, GloVe) and modeling (MultinomialNB)),  Property Address mapping, text summarization and classification

 - image classification (Transfer Learning) through more than 20 classes with F-score 97%

 - Speech recognition, auditory features extraction

 - GPT/LLM: gpt for regressor as experiment, text summarization, text paraphraze, titles for images and texts, objects on images

 - Strong analytical skills, ability to understand the essence of the issue, I have a master's degree in systems analysis, candidate for PhD, I like to modernize and improve existing processes