Position: Data Scientist
Category: Data Science
English level: fluent
Years of experience: 1.5

Before in my company pricing strategy was based on intuition only. I created a Linear Regression model that takes into account the elasticity coefficient. The model’s accuracy was 85%. It helped avoid the accumulation of products in the warehouse.
Landlord magazine (project job from March to October 2019) as Analyst

o Data normalization: Use Pandas library to cleaning and normalization of the EU land prices database to Machine Learning model building. 
o Data visualization: Data visualization to see how is an interconnection between data – land’s price and soil composition, climatic conditions, land location, etc.
o Statistical analyze: Statistical analyzes of the data to see data patterns and trends. 
o ML Model creating: Created a Random Forest model to predict Ukrainian land price after lifting the sale moratorium.
o Data analyzing: Based on the predicted land prices I wrote some articles about the future of the Ukrainian land market.

Good Logistics LLC (October 2018 – February 2019) as Marketer-analyst
o Statistical data analyzing: Made statistical analyze of the company’s clients, transportation volume, frequency. 
o Recommendations: Based on the analyze created marketing recommendations on how to increase the frequency and/or volume of transportation.
o Microsoft 365 for business: It’s integrated software, that brings together productivity tools, security, and management capabilities. Based on the sales managers' job I created recommendations on how to increase the frequency and/or volume of transportation of him/her clients.

Mary Kay (Ukraine) Ltd.  (April 2006 – May 2018) as Marketer-analyst
o Data standardization: Used Pandas to standardization and cleaning dataset to build predict model. 
o Created prediction model: Created a Linear Regression prediction model to predict sales. The model’s accuracy was 85%.
o Data visualizations: Using the Seaborn tool prepared graphics to identify patterns and trends before deeper analyzing sales data and consumer behavior. 
o Statistical analyze: Made statistical analyze of clients, product category sales.
o Clusterization: Using SPSS statistical package I built clusters of clients to create marketing activities for each of them. 
o Presentation for management: Analyzed prediction and clusterization models I created presentations for the company’s management to make decisions by them.
It can be a small team or a big project. It can be outsourced or a product company, new or used technology.