Team Lead, Decision Science
Posted 523 days ago, which is unusual. The employer's own board was still carrying it when we last read it, 2 hours ago.
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 2d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 523d ago
The date the source published, not the day we noticed it (2025-04-10). Last seen at its source 2h ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Nigeria, Russia
The description states no restriction of its own. This is the source's own tag.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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workable employer's own board first seen 10d ago · last seen 2h ago
The listing
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.