How to Become a AI Operational Risk Analyst

Analyze an existing credit scoring model's predictions for disparate impact across protected classes. Use fairness libraries to measure bias and propose and implement mitigation techniques like re-weighting or adversarial debiasing.

Build a complete CI/CD pipeline using GitHub Actions and MLflow for a simple model. Integrate automated data validation tests, model performance regression tests, and fairness checks as mandatory gates before model promotion.

Design and prototype a LangChain-based system that monitors an LLM chatbot's responses for potential compliance violations (e.g., incorrect advice) by checking outputs against a rule-based or smaller, trusted model.

Create a dashboard using Python (Plotly/Dash) or Tableau that visualizes key risk indicators for a sample ML model: accuracy over time, data drift scores, fairness metrics, and logged incidents.

Select an open-source model (e.g., a fraud detection model from Kaggle) and perform a full independent validation. Produce a comprehensive validation report, including findings on data, methodology, performance, and limitations.

Develop a script to simulate extreme market scenarios (e.g., flash crash, volatility spike) and analyze how a pre-trained market risk model's predictions change. Quantify potential P&L impact under stress.