Data analyst - 20057

●Utilized AdaBoost, Linear Regression, and Random Forest Classification algorithms in Python in forecasting regional branches' performance, achieving over 90% accuracy rate which led to improvements in resource allocation.

● Developed a model for the identification of branches for weekend/holiday operations via analyzing branch profitability using historical sales data, market trends & customer analysis. ● Developed Branch Rating System, leveraging quantitative metrics from both lending and non-lending product sales, standardizing performance evaluation & identifying top-performing branches for benchmarking initiatives.

● Implemented the K-Nearest Neighbors (KNN) technique in Python to cluster branches, stimulating intra-bank competition that resulted in higher performance of branches.

● Employed the Prophet Model in Python to generate accurate quarterly sales forecast reports for loan products, enhancing financial planning and strategic decision-making. 

● Led the analysis and redesign of core functional processes, achieving a reduction in process variations and eliminating 20% of non-value-added activities, resulting in streamlined.

● Identified and mitigated risks in business processes and systems by enforcing quality and integrity criteria, resulting in the development and implementation of solutions that boosted operational efficiency. 

● Prepared daily P&L and Balance Sheet reports annually, established cost tracking structures, and contributed to a 5% more accurate annual budget through in-depth cost analysis.

● Oversaw the performance of branches via a bespoke business map, crafted monthly and quarterly financial presentations, and compiled macroeconomic reports for the CEO and Supervisory Board. 

● Leveraged the CatBoost algorithm to forecast daily sales, subsequently setting strategic monthly targets aligned with projected values, achieved a model accuracy of over 90%

● Optimization of the number of branch employees by using simulation in order to get customer satisfaction

● Employed the DEA (Data Envelopment Analysis) model to assess branch performance, providing critical insights for management's strategic decision-making regarding branches.

● Forecasted volume of incoming calls to call centers to determine optimal service level and number of employees needed in order to achieve customer highest customer satisfaction level.

● Developed and automated Retail Sales dashboards using Tableau and Python, offering in-depth visualizations and key insights into retail sales, branch performance, customer feedback, and call center operations