I’m a Data Scientist with extensive expertise in Retail customer analytics, data management and reporting systems. I am passionate about analytics that drives insights to make business decisions. I enjoy working with people and driving challenging projects.
UNIVERSITY COLLEGE DUBLIN
Master of Computer Science
Major: Data Science
Relevant courses: Advanced Machine Learning, Project Management, Analytical Business Modelling, Text Analytics
MOSCOW INSTITUTE OF ELECTRONIC TECHNOLOGY BS in Microelectronic Engineering
GPA: 4.65 out of 5
Relevant courses: C++ programming, Database Management Activities and societies: volunteering work, students’ union
LLC CARDSMILE (Moscow, Analytic Service Provider) 2014– 2015 Customer Analyst
Worked as an analyst in a start-up that provides customer analytics for Retail clients.
- Implemented a BI reporting system for a retail chain (~280 stores/~2 million clients per year/~1 million transactions per
year). I drove an entire project myself (DWH architecture, ETL, reports) with a help from external BI consultants. New BI system consolidated data from multiple sources (CRM, sales and online store), in multiple formats (SQL database, json, xml) and used SAP Business Objects + Pentaho+ PostgreSQL.
Impact: the implementation of a BI system solved data inconsistency problems and reduced time spent on reporting.
- Built data mining models for clients. Developed Next-Product-To-Buy models for multiple clients: a grocery chain (~8 million of active customers per year), toys stores, jewelry stores.
Impact: switching from non-targeted campaigns to personal offers increased marketing campaigns response rates by 5%.
LLC LUKOIL-INTER-CARD (Moscow, Fuel Cards) 2011 – 2013 Customer Analyst in the Loyalty Department
Worked as a loyalty program analyst in the filling station chain (~1200 stations, ~1 million customers per year).
- Developed management reports to track main indicators (sales, average order size, churn rate, competitive pricing, call
Impact: report automation reduced time spent on reporting by 2 times.
- Build data-driven recommendations for marketing campaigns, significantly improved marketing efficiency:
– Implementedastandardizedapproachtoassesstheeffectivenessofmarketingcampaigns. – Developedcustomersegmentationsfortargetedmarketingcampaigns.
Impact: response rate was increased by 1.7 times and churn decreased by 30%.
- Developed a model for estimating the effectiveness of past marketing activities.
- Participated in developing the analytical part of the company’s marketing strategy.
- Data analysis: Python (NumPy, SciPy, Pandas, Scikit-Learn libraries), R, SAP InfiniteInsight, WEKA (academic purposes) Scripting languages: SQL (including query optimization), VBA
- Databases: PosgreSQL, MongoDB
- Dashboard Tools: SAP Business Objects, Tableau
- ETL Tools: Pentaho Data Integration