Qualifications, Experience & Competencies Required

• Bachelor’s degree in Computer Science, Data Science, Statistics, or a related field from a recognized institution.
  

• A master’s in data science, computer science, or a similar discipline is advantageous.
  

• Formal training in machine learning, artificial intelligence, data mining, or statistical modelling.
  

• Evidence of ongoing professional development through workshops, online courses, or industry certifications in emerging data science methodologies and tools
  

• Advanced Data Analysis & Modeling: Uncover hidden patterns and develop insights relevant to the bank and wallet operations using advanced statistical, quantitative, and Machine Learning methods. Design and implement robust, scalable machine learning models.
  

• Model Optimization & Monitoring: Optimize model performance and design automated solutions for model monitoring, including data drift detection and correction strategies.
  

• Explainable AI for Risk: Apply advanced analytics and AI/ML methods to explain the models and ensure transparency and interpretability.
  

• Framework Development: Develop scalable data science frameworks to enable efficient, build-once, deploy-many capabilities for various financial models.
  

• Production Integration: Develop and integrate machine learning algorithms into production-grade applications, particularly using FastAPI, ensuring robust, performant, and maintainable solutions.