Team Lead, Decision Science

This is a fully remote position, open to applicants in Nigeria.

📋 Description

• Advanced ML-modeling and data exploration: ensembles and AI algorithms, management of AI initiatives, and integration of external AI services. Emphasis on developing models and solutions for credit, fraud detection, marketing, collection and contact strategies, as well as text, speech, and behavioral analytics, dynamic pricing, and limits.

• Management of stakeholder expectations: engaging with the risk (portfolio) team, collection team, and other business units to discuss score modeling and backlog prioritization, along with clarifying tasks.

• Leadership of the data science team: overseeing recruitment, training, performance enhancement, scrum servicing, and task management. Enhancing communication within the data science team to better understand consumer and business needs.

• Management of environment, processes, and tools: including git, Jira board, Confluence content, and Agile rituals.

• Oversight of ML data management: collaborating with the DWH team; ensuring data availability, reliability, and quality; supporting and managing the integration of new and existing data sources, and controlling data flow stability, along with feature-store administration.

• Management of ML model lifecycle: from identifying business needs to deployment and production testing stages. Monitoring the stability of ML models and ensuring quality control, while reassessing and proactively improving quality through recalibration and rebuilding.

• Knowledge management: Keeping project documentation up-to-date, implementing standards, ensuring discipline, and maintaining accuracy for Confluence descriptions, feature-store metadata, git documentation, sharing internal experiences, and reviewing and implementing new methodologies and tools.

⛳️ Requirements

• Proven experience in the fintech or banking sectors, particularly in emerging markets.

• Practical experience in developing ML scoring models for text, speech, behavioral analytics, and dynamic modeling within card businesses.

• Strong programming abilities in Python and SQL for data analysis, modeling, and automation.

• Verified 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 application of AI concepts and systems.

• Familiarity with MLOps, covering data pipelines, model deployment, and the productionization of machine learning solutions.

• Experience with cloud computing platforms, particularly AWS (highly preferred).

• Proficiency in BI and data visualization tools such as PowerBI, Excel, Tableau, or Grafana.

• Prior exposure to risk management or analytics, especially in the cards or payments domain.

• Strong understanding of Agile and Scrum methodologies within a data or engineering context.

• Exceptional communication and presentation skills, capable of simplifying complex data concepts for both technical and non-technical audiences.

• Fluent in spoken English.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Opportunities for professional development and continuous learning.

• Flexible working hours and the possibility of remote work.

• Health and wellness benefits.