Lecha Makhmudov

> Formed and managed team of data analysts, including training new employees, setting tasks and communication with the stakeholders (product owners); > 3 ML models into production with visible resullts (double loan sales in marketing channel, car price prediction - 90% of cars assessed by model, churn prediction model in debit cards with actions) > Segmentizing customeres using statistical analysis which led to KPI's: - loan applications +10% in money; - contact rate -30%; - automated reports

> Synthesized current business intelligence data to produce reports and polished presentations, highlighting findings and recommending changes > Over 20 reports were optimized (time and memory optimization) > Built system of car recommendations based on clients' preferences; 
 > Developed database objects, including tables, views and materialized views using > Evaluated consistency and importance of different business intelligence data against needs to determine optimal courses of action

> Сollected data tables filled with historicity using SQL (automated) > Automatization of creating Life-Time Value model for retail company(not mentioned due to NDA restrictions) > Different small ad-hoc analyses for stakeholders

> Price plans launch cases for Value Proposition Department (active base, inactive base, ARPU, churn, revenue predictions + incremental analysis); > Structuring active price plans monitoring processes (building streams from scratch on IBM SPSS Modeler and providing stakeholders with presentations containing all the analyses results); > Different small ad-hoc analyses for stakeholders

> CRM analysis on SAS EG > Communications with clients/response model(cash loans) deployment into production using SAS EM > Communications with clients/response model(credit card) deployment into production using SAS EM > Churn prediction model deployment into production using SAS Enterprise Miner > Feature engineering – found indicators showing clients that are ready to buy Bank products > Led the project on implementation of Features table from the vendor > CLTV model (segmentizing customer database)

> Member of the team involved in the project on the implementation of the NPS system for the Bank; > Analysis of queues and AWT(average waiting time) in the entire branch network of the Bank; > Analysis of the performance of ATMs and payment terminals throughout the branch network of the Bank; > LCR calculations for the entire branch network of the Bank; > Analysis of call duration to the contact center of the Bank;