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.
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.
