Design, develop, and evaluate quantitative models used in retail lending analytics and optimization.
Apply reinforcement learning frameworks and/or optimization modeling techniques to decision based problems.
Analyze how models interact with computer systems, including inputs, outputs, and feedback loops.
Incorporate human-in-the-loop or other forms of human input models where applicable.
Test model behavior using different input structures and system configurations.
Document model assumptions, methodologies, results, and limitations.
Collaborate with technical teams to support model integration into production systems.
Support ongoing model enhancements, validation, and performance monitoring.

1–2 years of experience in quantitative modeling, reinforcement learning, optimization modeling, or related analytical work OR Bachelor’s degree (or higher) in Mathematics, Statistics, Quantitative Methods, Data Science, Computer Science, or a related field
Experience or academic exposure to reinforcement learning and/or optimization modeling techniques
Strong quantitative, mathematical, and statistical background
Understanding of human input models or unique/custom modeling approaches
Knowledge of how models interact with computer systems and software environments
Familiarity with how models accept, process, and respond to various forms of input data
Strong analytical thinking and problem solving skills

Quantitative Analytics – Model Analyst, Retail Lending Data Model Analytics

Key skills