You might be a strong candidate if you have:

• A Bachelor’s or Master’s degree in Data Science, Actuarial Science, Computer Science, Mathematics, Business Analytics, Economics, or a related quantitative field.
  
• 4+ years of experience in credit risk management roles, preferably within FinTech or modern consumer lending institutions.
  
• Demonstrated experience delivering data-based insights that informed business direction or program evaluation outcomes.
  
• Experience working in cross-functional environments with multiple stakeholders.
  

Technical Skills

• Strong proficiency in SQL and at least one data programming language (Python or R).
  
• Solid knowledge of statistical methods, regression techniques, and hypothesis testing.
  
• Familiarity with experimentation methods, including randomized controlled trials (RCTs) and quasi-experimental approaches.
  
• Experience with BI tools such as Tableau, Looker, or Power BI.
  
• Experience working with cloud data environments (AWS, GCP, or Azure) is an advantage.
  

Soft Skills

• Strong data reasoning and structured problem-solving ability.
  
• High attention to detail and dedication to data accuracy.
  
• Clear stakeholder communication skills (written and verbal).
  
• Ability to manage multiple priorities in a dynamic environment.
  
• Proactive approach with a strong sense of ownership and accountability.
  

• Review portfolio, credit, and collections performance to identify risk patterns and areas for improvement.
  
• Develop statistical models and testing frameworks (such as A/B testing) to improve credit policies.
  
• Design, track, and interpret Key Performance Indicators (KPIs) aligned with business priorities.
  
• Provide clear data insights to inform business planning and direction.
  
• Contribute to customer segmentation work and survey design to refine credit strategies.
  
• Monitor portfolio performance and strategic programs, identifying potential issues early to keep initiatives on track.
  
• Present insights to both technical and non-technical stakeholders in a clear and structured way.
  
• Translate complex statistical findings into practical business recommendations.
  
• Develop dashboards and reporting tools using Looker.
  
• Identify opportunities to simplify reporting and automate recurring data workflows.
  
• Contribute to the development of scalable data tools that improve operational efficiency.