Description Description- Assist in the development, validation, and maintenance of real-time fraud detection features.
- Design, build, evaluate, and defend machine learning models to predict fraud risk for Credit Karma Money products.
- Build efficient, reusable data pipelines for feature generation, model development, scoring, and reporting using Python, SQL, and CK's Machine Learning/AI infrastructure.
- Deploy models into production in partnership with data science and engineering teams.
- Collaborate with business stakeholders to translate model outputs into fraud policies and operational strategies.
- Implement model performance and monitoring metrics (e.g., AUC, KS, Gini) and stability indices (e.g., PSI, CSI).
- Ensure model fairness, interpretability, and compliance with regulatory frameworks, including FCRA and ECOA.
Requirements Requirements- Bachelor's degree in Mathematics, Statistics, Computer Science, or a related field.
- 2+ years of industry experience in Data Science, Machine Learning, or related areas.
- 2+ years of experience with Python and SQL:
- Strong Python proficiency, including libraries such as scikit-learn, XGBoost, LightGBM, pandas, and NumPy.
- Solid SQL skills for querying and transforming large datasets.
- Hands-on experience with a range of ML techniques, including tree-based models, regression, time series, causal analysis, and clustering.
- Ability to rapidly develop a deep statistical understanding of large, complex datasets.
- Experience in credit risk, lending, or fintech domains.
- Strong understanding of credit risk modeling concepts, including PD calibration, reject inference, adverse action logic, and risk segmentation.
- Experience working with tax and/or credit bureau data (TransUnion, Experian, Equifax) for credit model development.
- Familiarity with cash-flow data as an alternative or complementary data source.
- Strong business problem-solving, communication, and collaboration skills.
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