We are looking for a Data Science Manager with a strong background in managing data-driven solutions to lead a high-performing DS team within the banking sector. This role combines ML expertise, team leadership, and cross-functional communication, with a focus on scorecard development, model performance, and portfolio risk monitoring.

Responsibilities

• Advanced ML-modeling and data-exploration: ensembles and AI-algorithms, AI-initiatives management, external AI-services integration. Focus on models and solutions for: credit, fraud, marketing, collection and contact strategies, text, speech and behavioral analytics, dynamic pricing and limits.
• Stakeholders' expectations management: communication with risk (portfolio) team, collection team, other business units on score-modelling and backlog prioritization, task clarification.
• DS-team management: recruitment, training, performance improvement, scrum-servicing, task-management. Improvement DS-team communication with consumers and business needs understanding.
• Environment, process and tools management: git, Jira board, Confluence content, Agile rituals.
• ML-data management: colabration with DWH-team; data-availability, reliability and quality assessment; new/existent data-sources integrations support and management, data-flow stability control, feature-store administation.
• ML-model lifecycle management: from business needs identification to "sell", deployment and production-test stage. ML-models stability monitoring and quality control, reassessment and proactive quality improvement (re-calibration/ re-building).
• Knowledge management: Maintain up-to-date project documentation, implement standards, control discipline and maintain actuality for confluence descriptions, feature-store meta-data, git documentation, internal experience sharing and handover, new methodologies and tools review and implementation.

Requirements

• Demonstrated experience working in fintech or banking, especially within emerging markets.
• Hands-on experience developing ML scoring models for text, speech, behavioral analytics, and dynamic modeling in card businesses.
• Strong programming skills in Python and SQL for data analysis, modeling, and automation.
• Proven experience with machine learning techniques, including:
• Regression, classification, ranking, boosting
• Graph-based models, neural networks, NLP, and large language models (LLMs)
• Solid understanding and practical implementation of AI concepts and systems.
• Familiarity with MLOps, including data pipelines, model deployment, and productionizing machine learning solutions.
• Experience with cloud computing platforms, especially AWS (highly preferred).
• Proficiency with BI and data visualization tools such as PowerBI, Excel, Tableau, or Grafana.
• Prior exposure to risk management or analytics, particularly within the cards or payments space.
• Strong grasp of Agile and Scrum methodologies in a data or engineering environment.
• Excellent communication and presentation skills, with the ability to simplify complex data concepts for both technical and non-technical audiences.
• Fluent in spoken English.