Senior Data Scientist

Job Description

Role Summary

Hands-on data scientist on the data science and model development team, focused on building the machine learning and statistical models that power our consumer lending decisions. You will develop credit risk models for underwriting and portfolio risk decisioning, as well as models across fraud risk, marketing

response and propensity, customer lifecycle management, collections prioritization, account management,

and pricing, line, and offer optimization. You will execute and influence the model roadmap set with

business partners, owning the technical craft of individual models end to end: framing the problem,

engineering features, training and validating models, documenting your work for model risk review, and

shipping models that hold up in production. You turn data into reliable, well-governed models and translate their outputs into clear recommendations for the partners who own credit, fraud, marketing, and collections strategy.

Key Responsibilities

• Develop, validate, and maintain credit risk models for underwriting and portfolio risk decisioning

(scorecards, PD/loss and other supervised ML and statistical models)

• Build and support models across additional lending decisioning use cases as prioritized by business

partners, including fraud risk, marketing/propensity, customer lifecycle, collections prioritization,

account management, and pricing/line/offer optimization

• Own the hands-on model lifecycle for your work: problem framing, exploratory analysis, feature

engineering, model development, validation-ready documentation, and post-deployment monitoring

• Engineer features and evaluate alternative and third-party data sources (bureau, alternative credit, cash

flow/bank, device, and identity signals), quantifying lift, stability, cost, and compliance trade-offs

• Produce thorough model documentation and support model risk management and governance through

validation, audit, and review cycles

• Monitor model performance, drift, and stability in production (PSI, KS, AUC/Gini decay, calibration) and

diagnose degradation, recommending recalibration or rebuild when needed

• Design and execute champion/challenger tests, backtests, and holdout analyses to measure model

impact on approvals, losses, response, and downstream portfolio performance

• Apply explainability methods (e.g., SHAP, reason-code generation) and build fair lending and regulatory

awareness into model design and feature selection (disparate-impact considerations, adverse

action/reason codes)

• Partner with data engineering and decisioning-platform teams to productionize models, evolve features

and pipelines, and ensure reliable real-time and batch scoring

• Translate model outputs into actionable business recommendations—connecting scores and segments

to the policies, offers, and operational actions owned by credit risk, fraud strategy, marketing,

collections, product, and other stakeholders

• Contribute to analytical standards, reproducibility, and tooling (code review, version control, experiment

tracking) and help raise the technical bar across the team

• Help shape and refine the model roadmap by surfacing opportunities, sizing impact, and prioritizing

work with the Director, Data Science and business partners

Qualifications

• 4-6 years in data science, statistical modeling, credit risk analytics, or a closely related quantitative field,

with hands-on development of models used in real decisioning

• Strong, current hands-on Python and SQL skills used to build, evaluate, and ship models

• Command of supervised machine-learning and statistical methods (e.g., gradient boosting,

logistic/regularized regression, tree-based models, validation and feature-selection techniques)

• Demonstrated experience across the model lifecycle: feature engineering, model development,

validation-ready documentation, and monitoring of drift and performance

• Experience with credit risk or consumer lending models preferred (scorecards, PD/loss, or related

decisioning models)

• Ability to communicate complex modeling concepts, assumptions, and trade-offs clearly to both

technical and non-technical stakeholders, and to translate results into recommendations

• Bachelor's degree in a quantitative field (Statistics, Computer Science, Mathematics, Economics,

Engineering, or related); advanced degree a plus

Preferred Qualifications

• Consumer lending experience including familiarity with US consumer lending regulations and risk

management practices

• Familiarity with regulatory and model risk expectations relevant to lending models (e.g., FCRA,

ECOA/Reg B fair lending, SR 11-7-style model risk management)

• Experience building or supporting models beyond credit risk, such as fraud, marketing/propensity,

collections, customer lifecycle, or pricing/line optimization

• Hands-on experience evaluating alternative and third-party data and integrating models into a real-time

decisioning platform

• Familiarity with explainability methods (SHAP, reason-code generation) and fair-lending/disparate

impact testing

• Experience with modern ML/MLOps tooling and reproducible, version-controlled modeling workflows