About me
I’m a results-driven digital marketing professional with over 8 years of experience in leading marketing strategies for e-commerce and tech companies. I've worked both in established organizations managing multimillion-euro budgets and in startups, designing and launching strategies from scratch. I focus on improving ROI and LTV while driving sustainable growth.
I collaborate closely with product and sales teams to align marketing efforts with business goals. I thrive on solving complex challenges, optimizing campaigns, and delivering measurable results.
Work Experience
Marketing Consultant
- • Cofounded a startup focused on developing an AI-powered chatbot for the people who want to learn relocate.
- • Conducted market research and qualitative research of the target audience.
- • Created go-to-market strategy.
Lead Performance Marketing Manager
- • Implemented a data-driven approach by continuously conducting A/B testing in order to optimize advertising campaigns. This resulted in achieving a 26% YOY growth in ROAS and a 19% YOY increase in buyers.
- • Managed agency partnerships, boosting ROAS by 34 percentage points and effectively scaling budgets by 28% within 6 months.
- • Development and launch of a promotion strategy for the new product in digital channels. As a result, the channel's share in lead generation >30%.
- • Conducted market research and analyzed consumer preferences to identify and test new traffic channels, adding 2% to revenue with an ROAS above 250%.
- • Implemented and scaled UGC content strategy across promotion channels, achieving 15% increase in app-installs.
Performance Marketing Manager
- • Conducted thorough competitor research and developed a promotion strategy for the European market, driving 83% growth in orders and reducing the Cost Per Order by 58% within the first year.
- • Enhanced remarketing efforts, achieving a 38% increase in cross-sales.
- • Redesigned search campaigns and optimized keywords on Google Ads, leading to a 41% reduction in CPO.
- • Developed and launched paid social campaigns on FB ads, resulting in sales increase by 33% within the allocated budget
- • Stayed up-to-date with industry trends, continually testing and adopting new techniques that led to a 36% improvement in overall campaign performance
SEM Specialist
- • Achieved a 62% increase in orders on the hypermarket "Globus" website while maintaining a strong ROAS.
- • Reduced CPL to 29% by conducting A/B tests and implementing optimizations for educational courses.
- • Successfully managed up to 10 simultaneous projects spanning e-commerce, services, and education sectors, showcasing strong multitasking and project management skills.
Sales and Marketing Intern
- • Assisted in organizing and promoting events.
- • Prepared marketing reports and presentations for senior staff.
Education
Plekhanov Russian University of Economics
Professional Development
Data-driven Product Management Simulator
GoPractice Inc • 2025
Google Ads - Measurement Certification
Google Digital Academy (Skillshop) • 2024
Google Prompting Essentials
Google • 2024
AI for Marketing
Coursera • 2024
Marketing Analytics
University of Virginia • 2024
Basic SQL
Avito training center • 2022 - 2023
Effective management
Avito training center • 2021 - 2021
Performance-marketer
Netology training center • 2020 - 2021
Publications
Analyzing Cultural Biases and Socioeconomic Influences in Online Ratings
View Paper- • Showed that countries socioeconomic factors influence online ratings.
- • Discovered that hotel guests from English-speaking countries tend to give higher and less varied ratings, than predominantly Muslim countries that use a wider range of scores resulting in lower average ratings.
- • Showed that hotel scores were most predictive of Hofstede's cultural dimensions of Individualism vs. Collectivism and Long-Term Orientation vs. Short-Term Normative Orientation.
Complementing the classification of SnP500 companies using investors' preferences
- • The industry a company is classified to in SnP500, is often different from the investors' perception of the company's industry.
- • A simple Bayesian model predicts companies section (1 out of 11) with 47% accuracy rate.
- • If investors are split into groups with an equal number of sentiment scores given by less active analysts, high active analysts and critics, then the former provide more informative sector prediction models.