Position: Data Scientist
Category: Data Science
English level: upper
Years of experience: 0.5


Self-driven, motivated, and dedicated Data Scientist with 3 years of experience working as a Data Analyst, passionate about helping businesses succeed. I would like to participate in interesting and perspective projects and apply the machine learning algorithms in practice. Fast learner, sociable and responsible, I strive for development and improvement of professional skills.

Qualifications:
- Experience in data gathering, preprocessing, and data analysis.
- Understanding of classical Machine Learning algorithms and their application in practice.
- Knowledge of Python (NumPy, Pandas, Scikit-learn, SciPy, Matplotlib, Seaborn, Plotly).
- Knowledge of SQL, RDBMS (Microsoft SQL Server, PostgreSQL, MySQL).
- Experience in data visualization, designing reports and dashboards using PowerBI, Tableau, and Microsoft Excel.
- Experience with Jupyter Notebook, Visual Studio Code, PyCharm, Git, and Jira.
- Knowledge of Exploratory Data Analysis (EDA).
- Knowledge of evaluating the quality of the ML algorithms, hyperparameter optimization.
- Understanding of the basic principles of organizing and conducting A/B testing.
- Knowledge of mathematical analysis, linear algebra, statistics and probability theory.
- Strong time management skills, with the ability to prioritize and multitask.
- Strong communication and creative problem solving skills.

While I was working as a Data Analyst, my responsibilities included:
- Maintaining internal systems, collecting and parsing data from internal and external resources using Python and SQL queries (Microsoft SQL Server).
- Developing dashboards and reports using data visualization tools such as PowerBI and MS Excel.
- Reducing the time for preparing reports due to the development and implementation of automated reporting.
- Measuring and demonstrating organizational efficiencies with data analysis and reporting.
- Performing regular and ad-hoc analysis and presenting results to stakeholders.
- Building the reporting infrastructure to provide real-time insights into the business KPIs.
- Effectively prioritizing and executing tasks while under deadlines.