Credit risk model manager

15 hours ago

Islamabad, Islamabad, Pakistan Dyna Full time

Job Responsibilities

  1. End-to-End Model Development & Deployment
  • Independently design, develop, and deploy risk models (e.g., anti-fraud, credit assessment, credit limit models, behavior scoring, collection scoring) and marketing models (e.g., campaign response, churn prediction) using algorithms including LR, tree-based ML, neural networks, etc.
  • Optimize existing models; lead consulting projects from planning to delivery, including solution design, timeline management, quality control, reporting, and client presentations.
  1. Risk Strategy Optimization
  • Analyze client approval workflows and risk performance to design/optimize pre-approval, in-process, and post-approval risk strategies.
  • Advise on customer segmentation, automated approval rate improvement, targeted marketing tactics, and channel management.
  1. Client Consultation & Solution Design
  • Collaborate with sales to address client pain points, deliver quantifiable solutions, and present business cases.
  • Diagnose risk/operational gaps in lending institutions, integrating industry trends and regulatory shifts to drive actionable solutions.
  1. Innovation & Industry Leadership
  • Research and implement cutting-edge algorithms (e.g., GPT) to develop data-driven risk/operational products.
  • Design novel solutions for auto finance risk/marketing challenges and pilot their implementation.

Qualifications

  1. Education
  • Master's degree or higher in Mathematics, Statistics, Computer Science, or related fields.
  • Graduates from top-tier universities preferred.
  1. Experience
  • 5+ years in model/strategy development at banks, licensed consumer finance firms, leading fintechs, or financial institutions.
  • Priority given to candidates with:
  • Auto finance experience
  • Fintech project management
  • Project management certification (e.g., PMP)
  1. Technical Skills
  • Proficiency in Python/Spark/R for data mining and modeling.
  • Hands-on experience with LR, tree-based ML, neural networks, or deep learning algorithms.
  1. Industry Knowledge
  • Deep understanding of retail credit markets, product structures, and end-to-end risk management lifecycle.
  • Familiarity with regulatory frameworks (e.g., Basel III, local compliance standards).
  1. Soft Skills
  • Analytical thinker with strong problem-solving abilities and data sensitivity.
  • Self-driven, adaptable to new technologies, and committed to innovation.
  • Diligent, meticulous, and thrives in challenging environments.
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