Position: Data Analyst
Category: Data Analyst
English level: upper
Years of experience: 8

Preparation of statistical reports and data visualizations for management board. A detailed review of credit risk and anti-fraud decision rules used in loan origination processes for complete product portfolio (SPL, Installment), optimizing it according to world best practices (have experience with experts from FICO, KPMG, AdAstra). Increasing AR with keeping quality of booked loans. Building RBP (risk-based pricing) models. Participate in building risk DWH. Automatisation of dynamic reports, using MS SQL Server with Power BI. Using data mining tools and algorithms to loan origination processes.

Development, validation, and maintenance of new scoring models (applicational and behavioral). Regular monitoring and analysis of the effectiveness of the scoring model. Making proposals for changes to the scorecards. Review of the
validation tests. Assessment of the results of the changes in the financials of the business. Calibrations of the scoring models when needed. Internal models’ documentation. Deployment of scorecards; Analysis of the applicability of external fraud-prevention services and scoring models in the business; Analysis and regular monitoring of existing scoring models and external/internal tools used in the decision-making process. Analysis of their effectiveness.

Creating models with alternative data, such as transaction data, behavioral data, etc.

Creating Risk DWH. ETL/ELT processes.

Assisting in creating MIS needed for risk reporting, development, and maintenance
of scoring models; Liaison with members of international teams; Assistance in DWH and MIS development.
Worked as a data analyst in different finance organizations. Main responsibilities: 
- building machine learning models to prevent fraudulent activities;
- creating optimal decision-making processes (data-driven);
- different kinds of reports (mostly Power BI and Excel, a little bit of Shiny);
- creating and maintaining DWH;
- ad-hoc analytics in marketing (LTV, churn, retention), finance (unit economics, uplift models);
- other (ETL, model deployment, web services);

My main stack of technologies:
- Python (all ML libraries);
- R (tidyverse, shiny);
- SQL (mostly PostgreSQL);
- Power BI, Excel;
- Talend (for ETLs);
- Some web development tools (HTML, CSS, JavaScript, jQuery);

I'm looking for interesting projects or permanent work in companies/startups. My main interests are in Machine Learning, Data Analytics, Web Development.