Position: Data Analyst
Category: Data Analyst
English level: fluent
Years of experience: 5

- Bachelor of Business Administration (Graduate of 2019)
- Microsoft Certified: Power BI Data Analyst Associate
- Certified Python Programmer - [PCEP-30-02]
- Data Analyst with Python Track (DataCamp)
- Work and Travel program experience at USA
- Google Analytics Individual Qualification
- Business Strategy Game winner (Jan 2017)
I have around 5 years of experience in different industries including telecommunications, healthcare, life insurance and railways transportation as a Data Analyst. I am also Microsoft Certified Power BI Data Analyst.

 Main tools that I have work experience:

- Power BI
- Python (pandas, numpy, seaborn, scikit-learn, statsmodels)
- R (data.table, dplyr, caret, xgboost, lubridate)
- SQL
- Tableau
- DBT
- Google Cloud Platform (BigQuery)
- Looker (lookml) / Looker Studio
- Microsoft Excel.

Main accomplishments:

- Designed the data model by using the various ETL tools of Power Query and created reports and dashboards to track main company KPIs by utilizing advanced calculations, parameters, maps, groups and hierarchies (Power BI, Tableau)

- Built and maintained ETL pipelines by using DBT, GCP, BigQuery cloud technologies and create analytical dashboards in Looker/Looker Studio

- Implemented data hygiene measures and practices across large data sets like data cleaning, deduplication, data quality measurements in a fast-paced agile startup environment (SQL, Python)
- Prepared churn model which could predict 78% of churners 4 months in advance by using Xgboost, Balanced Random Forest, SVM and KNN algorithms in ensemble model

- Segmented all customers into 7 distinct segments by using k-means algorithm on which CVM team could launch different campaigns depending on each segment’s main KPIs

- Automate different sales and marketing related reports to run on a daily/monthly basis by using Python, R and SQL languages

- Became advanced user of analytics tools for making analysis and also automated various reports to run them on a daily/monthly basis (Python - pandas, numpy, seaborn, R - dplyr, data.table, openxlsx, lubridate)