data scientist for advertising monetization

The company develops mobile products with millions of users, focusing on advertising monetization, recommendation systems, personalization, and core product algorithms.

• Lead Data Science initiatives focused on advertising monetization and maximize revenue while maintaining a strong user experience;
• Develop ML models for ad targeting, ranking, pricing, revenue prediction, and monetization optimization;
• Improve recommendation and personalization systems to increase user engagement, retention, and lifetime value;
• Analyze user behavior and advertising performance to identify growth opportunities and drive product decisions;
• Design, analyze, and interpret A/B tests and other experiments;
• Collaborate with Product Managers, Engineers, and Analysts to define the roadmap and prioritize high-impact initiatives;
• Mentor Data Scientists through technical guidance, code reviews, and best practices;
• Drive the adoption of scalable ML solutions and ensure high standards for experimentation and model quality;
• Present insights and technical solutions clearly to technical and non-technical stakeholders.

• 5+ Years of experience in Data Science, preferably in consumer mobile products;
• Proven experience with advertising monetization, revenue optimization, or advertising analytics;
• Strong experience building recommendation, personalization, or ranking systems;
• Advanced proficiency in Python and SQL;
• Hands-on experience with machine learning models, including classification, regression, ranking, forecasting, and predictive modeling;
• Strong understanding of experimentation methodology and applied statistics;
• Experience working with large-scale behavioral datasets;
• Experience leading or mentoring Data Scientists and driving technical initiatives;
• Ability to translate business goals into ML solutions with measurable impact;
• Native Russian language;
• Nice to have: experience with mobile advertising ecosystems, recommendation systems based on embeddings, collaborative filtering or learning-to-rank, deploying ML models into production, LLMs or GenAI applications, MLOps and cloud platforms.

• Competitive salary and benefits package;
• Flexible working hours;
• Hybrid/office work options;
• Career growth and professional development opportunities.