Head Of Analytics jobs in Canada

We represent a technology player whose digital banking platform is transforming financial services in emerging markets making a real impact by embedding credit and savings products into the digital channels people use every day. Their data-driven technology powers MNOs, fintechs, and banks enabling them to scale fast and drive financial inclusion for millions. For those looking to work on cutting-edge financial tech with real-world impact, this is the opportunity for you. With rapid growth, industry recognition, and a team that thrives on innovation, this is a chance to shape the future of finance in high-growth markets across Africa.
Job Description:
The Data Science and Analytics Lead will enhance their credit risk modelling, predictive analytics, and operational efficiency. Improve multi-source data integration, strengthening model governance, automating data processes, and scaling our infrastructure for faster decision-making and market expansion. The Data Science and Analytics Lead will drive data-driven insights, refine risk models, and build scalable solutions that optimize lending decisions and business growth.
Your daily adventures include:
Strategic Leadership & Business Impact

• Drive the development of the data science strategy to support business growth, embedded finance, and new market expansion.
  
• Leverage AI and behavioral science insights to enhance credit performance, customer engagement, and savings adoption.
  
• Partner with product, risk, and engineering teams to integrate data science into decision-making and operational processes.
  

• Build, refine, and deploy AI-powered credit scoring models ensuring high performance, fairness, and explainability.
  
• Lead experimentation and A/B testing initiatives to enhance underwriting, portfolio management, and product innovation.
  

• Collaborate with Engineering to expand data science capabilities to support multiple markets, optimizing for scalability and adaptability.
  
• Ensure data integrity, security, and compliance across all data science initiatives.
  

• Drive best practices in model development, MLOps, and responsible AI.
  
• Promote cross-functional collaboration to maximize the value of data science across the organization.