Position: Data Analyst, Data Scientist
Category: Data Analyst
English level: upper
Years of experience: 1

Implemented ML algorithm: Linear regression for prediction revenues from special month events with Python and its libraries
Reported directly to CEO: for designing and developing
the dashboard about the communication rate of company
recruiters' which led to boosting the company's rate on
Handshake from 50 to 65%
Designed and integrated Tableau reporting
Completely automated mid-, end- month marketing reporting 
Optimized marketing metrics by adding ROAS, led to the growth of the number of special events and percent of company penetration into new marketing regions
I've worked for a year in this field as a Data Analyst, Business Intelligence for USA companies.
I grew from a Statistical intern to a Data Analyst. 
I used my experience to bring new approaches to marketing analysis. For example, I've changed Excel tools on Python and Excel visualization to Tableau reporting.
However, my responsibilities included not only analyzing business problems and reporting them with data visualization tools but also finding a solution to these problems. 
Machine learning algorithms are the most exciting part of my work. For example, I used Linear Regration to predict
revenues from special marketing events.
Furthermore, I have experience with both Supervised, such as Linear, Logistic Regression, SVM, KNN, and Unsupervised like K- means, K-medians clustering ML algorithm. I can accomplish Decision Trees or Neural Networks. In practice, I prefer to combine them to get more precise predictions without overfitting.
Among other skills, I'd like to highlight Python and its libraries: Flask (SQLAlchemy), Pandas,
Numpy, Seaborn, Matplotlib, Sklearn, Pytorch; PostgreSQL; Git, and Docker. I really like Linear
Algebra, I'm good at statistics and the theory of probabilities(my first course about the game
theory I took at tenth grade), and of course, I'm familiar with the basics of calculus.
I'm learning Data Science for more than two years and not gonna stopping as one of my goals is to
receive a university education in math. My background as a legal researcher gives me a much deeper understanding of formal logic and is fundamental for analytical thinking, and data orientation. I appreciate my law education for letting me practice mediation. Now as a Data
Scientist, it allows me to explain hard stuff easily and find solutions that would satisfy all stakeholders.