Risk Data Analyst
Toronto, ON

Job Description

CogniFrame is a hybrid machine learning company that combines Classical Risk Models and Quantum Optimization to drive lower delinquencies and a higher Return on Assets for our clients. Offered under a SaaS model, the CogniFrame platform meets the stringent security requirements of financial institutions. We are a start up but actively expanding our platform into quantum computing. To support our growth, we are currently looking to hire an candidate preferably with 1-2 years of risk modelling experience.

Position Responsibilities

  • Help develop predictive models for credit risk management and asset optimization
  • Develop behavior scoring models for various lending and asset products
  • Develop, implement & monitor models that incorporates forward looking macroeconomic scenarios.
  • Understanding of AI and Machine Learning approaches to modelling customer behavior is an advantage
  • Participate in design, planning, implementation and testing of various modelling initiatives
  • Prepare comprehensive model development and deployment documentation and monitor model performance
  • Perform ongoing calibration of models by executing extensive model testing strategies
  • Develop analytics and insights using data sources

Position Requirements

Bachelor or Master's degree required in Economics, Statistics, Math or equivalent with 1-2 years in credit risk modelling within a financial institution or fintech would be ideal. Knowledge of capital adequacy and solvency solutions is advantageous. A solid understanding of behavior score and modelling techniques is a must. Excellent written / verbal communication and professional maturity to explain complex technical concepts to business partners who are non-technical & clear and concise data visualization and presentation skills are required. Self motivation and high level of initiative to prioritize work amid competing demands and take projects from the briefing phase to the delivery of final reports/presentations with minimal supervision. Must be a strong team player who is keen to learn as well as contribute in an environment of ambiguity and embraces change. Some understanding of the Predictive Modelling Life cycle, model development and tuning, model validation, model management, performance monitoring, data mining, segmentation and statistical model development is required.

Interested? Please send us a detailed resume along with a cover letter explaining your suitability for this role.

Job Type: Full-time


  • risk modelling: 1 year (Preferred)