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


Skills: Statistical analysis (9 years), R (6 years), Machine learning (6 years), Shiny (4 years),  Python (3 years), SQL (3 years)

Experience (top-3 largest projects):
1) Predicting employee attrition (survival analysis + binary classification + testing various hypotheses). This DS project was the core of an HR application for a company with over 70,000 employees.
Algorithms: cox regression, random forest modification for survival analysis, and default ML algorithms (Catboost). Statistical tests: t-test, chi-square, Mann-Whitney, Kruskal Wallis. 
Creating ML pipeline with auto- collecting data / tuning model / training model every month based on Docker and publishing in Microsoft Azure. 
Creating API based on Azure Functions.
Creating dashboard based on Dash with the results of training and tuning models.

2) The prediction of labor protection's rules breakers (binary classification + testing various hypotheses). Algorithms: glm, catboost. Statistical tests: t-test, chi-square, Mann-Whitney, Kruskal Wallis, ANOVA. Creating a web-application via shiny, creating reports with data visualization in html via markdown. 

3. Energy cost prediction (regression). Algorithms: xgboost. Creating reports with data visualization in html via markdown. 

Education: PhD in Physical and Mathematical Sciences.
Interesting tasks, new experience, professional growth in ML/AI.