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AI Credit Model Adverse Action Reason Engineer

Noah Kovacs

Full-time · Job · New_York

About the role

Our explainability team has five people: two data scientists, a compliance specialist, a designer and a product lead. We build tools lenders use to explain credit model decisions to regulators and applicants. When an application is declined, US lenders must give specific reasons. With complex models, those reasons are often vague or inconsistent between similar applicants. Our product generates adverse action reasons from feature attributions, and lenders now want them more stable, more readable and easier to defend in exams. We need someone who can improve both the method and the product around it.

Responsibilities

  • Improve the stability of reason codes generated from model attributions
  • Define product requirements with lenders and our compliance specialist
  • Build test suites that check reason consistency across similar applicants
  • Track model and reason-code versions in MLflow for audit purposes

Requirements

  • Experience with credit risk models or consumer lending analytics
  • Hands-on with SHAP or other attribution methods
  • Understanding of ECOA, Regulation B or similar fair lending rules
  • Strong Python and SQL skills

Benefits

  • Fully remote across US time zones
  • Annual bonus
  • 401(k) match
  • Comprehensive health coverage

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Categories

AI Product & Strategy data science & analytics

Skills

python scikit-learn statistics sql mlflow

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$130k – $160k / year

AI Expertise

AI Product & Strategy data science & analytics

Skills

python scikit-learn statistics sql mlflow

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