Data Scientist, Robert Half

Robert Half

Not provided

Save Job

Design, build, and iterate on supervised and unsupervised ML models (tree-based, regression, time-series, causal, clustering) to predict credit risk and optimize lending outcomes
Defend model choices and validation results with statistical rigor
track AUC, KS, Gini, PSI, and CSI to monitor performance and stability
Build reusable Python/SQL data pipelines for feature generation, model training, scoring, and reporting on ML & AI platform
Deploy and maintain models in collaboration with peer data scientists and ML engineers, ensuring robust CI/CD and automated monitoring
Translate model outputs into credit policy recommendations—PD calibration, reject inference, adverse-action logic, risk segmentation
Work with risk and product teams to align decision strategies with business goals and regulatory constraints (FCRA, ECOA, etc.)
Champion model interpretability and fairness audits
document assumptions, limitations, and controls to satisfy internal governance and external regulators

Bachelor’s or higher degree in Mathematics, Statistics, Computer Science, or a related field
2+ years of industry experience in Data Science or Machine Learning
2+ years of hands-on experience with Python (pandas, numpy, scikit-learn, XGBoost, LightGBM)
Strong SQL skills for large-scale data extraction and transformation
Deep understanding of machine learning techniques including tree-based models, regression, time series, causal inference, and clustering
Ability to quickly gain statistical insight from large, complex datasets
Experience in credit risk, lending, or the fintech domain
Knowledge of credit risk modeling concepts such as PD calibration, reject inference, adverse action logic, and risk segmentation
Experience working with tax and/or credit bureau data (e.g., TransUnion, Experian, Equifax)
Familiarity with cash-flow data as an alternative or complementary source
Strong business acumen and problem-solving ability
Excellent written and verbal communication skills and a collaborative mindset

Experience with internal ML infrastructure or similar platforms
Familiarity with fairness and model explainability tools (e.g., SHAP, LIME)
Exposure to short-term lending products like BNPL, tax refund advances, or POS installment loans

What we offer:

Employment Type:

Fulltime

Work Type:

On-site work

Job Link Share: