• Data Scientist

    Robert Half Technology (Charlotte, NC)


    Description

    Job Description:

    We are seeking a highly skilled Credit Risk Data Scientist to design, build, and deploy machine learning models that drive credit decisions for short-term lending products such as tax refund advances, BNPL, and installment loans. You will collaborate with product, risk, and engineering teams to ensure models align with business goals, deliver actionable insights, and meet regulatory standards.

    Key Responsibilities:

    + Design, build, evaluate, and defend machine learning models to predict credit risk.

    + Build efficient and reusable data pipelines for:

    + Feature generation

    + Model development

    + Scoring and reporting

    + Work hands-on with Python and SQL using cloud-based ML infrastructure (e.g., CK ML/AI tools).

    + Deploy models in a production environment with engineering support.

    + Collaborate with business stakeholders to implement model-driven lending policies.

    + Apply model performance metrics (AUC, KS, Gini) and monitor model stability (PSI, CSI).

    + Ensure models meet fairness, interpretability, and compliance standards (FCRA, ECOA, etc.).

    Requirements

    Required Qualifications:

    + Strong hands-on experience in Python and SQL for data manipulation and modeling.

    + Proven ability to create, manage, and implement data tables.

    + Solid experience building machine learning models, especially tree-based algorithms (e.g., XGBoost, Gradient Boosted Machines, Random Forest).

    + Experience building data pipelines for model training and scoring.

    + Experience using credit bureau data and cash flow data for model development is highly preferred.

    + Familiarity with regulatory/compliance frameworks (FCRA, ECOA); helpful but not as critical as in Role 1681.

    + Background in installment and short-term lending is a plus.

    + “Fingers on keyboard” mentality — must be actively coding and building models.

    + Ability to work independently in a fully remote setting.

    + Experience with fairness and interpretability in ML models is a plus.

     

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