Position: Data Scientist
Category: Data Science
English level: upper
Years of experience: 5

- have a publication in the NLP domain on EACL
- Won a few ML hackathons
- took part in a lot of different learning and community activities
Building a system that can score how likely leads/prospects will convert for the insurtech company to reduce costs and increase efficiency. Worked on:
 Preparing data
 Business understanding of data
 Preprocessing and generating features
 Training binary classification models
 Creating a prediction pipeline
 Analysing historical data about telemarketing calls and creating an algorithm to detect performance degradation for phone numbers.
Tools and technologies: CatBoost, LightGBM, XGBoost, FB Prophet, numpy, pandas, scikit-learn, scipy, SnowFlake, AWS SageMaker


Two parts of a project:
 Building a test results management tool
to filter false-positive errors and reducing
the cost of testing.
 Experimenting with an automatic tool
to detect, interact with, and check all the objects on the web page.
Worked on:
 Collected and processed raw data from the data warehouse.
 Generated features for analyzing different errors.
 Built and tested statistical hypotheses.
 Built and validated models.
 Generated the model's results reports.
 Built a pipeline for retraining a models
 Built an automated tool for Windows.
 Developed a logic for object detection
and interaction.
Tools and technologies: pandas, CatBoost, Keras, scikit-learn, OpenCV, win32auto, selenium, Flask, OracleDB.
1) Interesting and challenging project withreal world applications.
2) Motivated team that is constantly learning something new and developing its expertise.