Data Science and Analytics Lead

Titc; Dubai; On-site; AED 120,000 - 180,000; Full time

Job summary

An innovative technology player is seeking a Data Science and Analytics Lead to enhance credit risk modeling and drive data-driven insights. This role involves strategic leadership, AI governance, and optimizing data infrastructure to support business growth in emerging markets. You will collaborate with cross-functional teams to integrate data science into decision-making processes, ensuring high performance and compliance. If you are passionate about leveraging cutting-edge technology to make a real-world impact in the financial sector, this is the perfect opportunity for you to shape the future of finance.

Qualifications

• Strong background in data science, machine learning, and AI.
• Experience in credit risk modelling and financial services.

Responsibilities

• Drive the development of data science strategy for business growth.
• Build and deploy AI-powered credit scoring models.

Skills

Data Science

Machine Learning

AI Governance

Credit Risk Modelling

Analytical Skills

Leadership

Education

Bachelor's degree in a quantitative field

Tools

AWS

GCP

Azure

Job description

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.
  

Model Development & AI Governance

• 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.
  

Data Infrastructure & Scalability

• 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.
  

Team Leadership & Development

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

Requirements
What it takes to succeed:

• Strong background in data science, machine learning, and AI with experience in credit risk modelling and financial services.
  
• Deep understanding of AI governance, model transparency, and regulatory compliance in financial services.
  
• Hands-on experience with MLOps, model deployment, and automated monitoring solutions.
  
• Strong analytical mindset with a proven ability to drive business impact through data science.
  
• Excellent leadership skills with the ability to mentor and build high-performing teams.
  
• Bachelor's degree in a quantitative field such as Statistics, Mathematics, Physics, Computer Science, Data Science, Engineering, Economics, Financial Engineering, Actuarial Science, or a related discipline.
  
• Experience in fintech, digital lending, or embedded finance is preferred.
  
• Exposure to cloud platforms such as AWS, GCP, or Azure for data engineering and machine learning is beneficial.
  
• Familiarity with graph analytics, network science, or behavioral data modelling is beneficial.
  
• Knowledge of causal inference techniques and advanced experimentation methodologies is beneficial.
  
• Prior experience in expanding data science functions into new markets is beneficial.