Senior Data Scientist - Alternative Data & AFS Solutions (Non-Prime Lending)

Experian

About The Position

We are looking for an experienced Senior Data Scientist to join our Alternative Financial Services (AFS) department, supporting clients in the non‑prime and near‑prime lending markets. You'll design custom analytics, credit strategies, and machine‑learning models using both traditional and alternative data. This is a client‑facing, solution‑oriented role requiring technical depth and the ability to convert complex analyses into practical, applicable recommendations. You will report to the VP of Analytics Product Build, Innovation, and Scores. You'll have opportunity to: Lead custom analytics and modeling engagements from scoping through delivery and ongoing support. Develop credit strategies and ML models (underwriting, line assignment, pricing, early warning, collections). Engineer features from alternative, transactional, and bureau data (e.g., recency, frequency, volatility, trend, and behavioral metrics). Evaluate and integrate third‑party/alternative data sources (sub‑prime bureaus, cash-flow, telco, utility, and specialty data). Conduct segmentation, lift analysis, and champion/challenger testing to assess performance and incremental value. Develop custom scorecards, policy rules, and ML models aligned with each client's risk appetite and regulatory requirements. Partner with clients to build end‑to‑end credit strategies that balance approvals, losses, efficiency, and customer experience. Deliver clear, executive‑ready insights, documentation, and strategy recommendations. Present results directly to risk leaders, analytics teams, and senior client partners. Support model implementation, monitoring, stability analysis, and ongoing optimization. Work cross‑functionally with Product, Engineering, and Sales to align custom solutions with broader AFS capabilities. Contribute to AFS best practices, reusable frameworks, and internal accelerators for non‑prime analytics.

Requirements

5+ years in credit risk analytics, data science, or advanced analytics, with experience in non‑prime or near‑prime lending.
Hands‑on modeling experience using alternative data.
Proficiency in Python (Pandas, NumPy, scikit‑learn, XGBoost/LightGBM) for feature engineering, modeling, and analysis.
Advanced SQL experience working with complex, and imperfect datasets.
Experience with non‑prime risk dynamics: thin‑file consumers, volatility, fraud risk, early‑default behavior.
Experience with model evaluation (AUC, KS, lift, bad‑rate curves, stability, PSI).
Work directly with clients and translate analytics into deployable strategies.
Explain complex models in clear business terms.
Background in financial services, alternative lending, FinTech, or specialty finance.

Nice To Haves

Experience with AFS data sources (Clarity, FactorTrust, MicroBilt, cash‑flow or specialty bureaus).
Familiarity with model governance, explainability, and regulatory considerations in non‑prime lending.
Experience deploying or supporting ML models in production environments.
Exposure to fraud, identity, or first‑payment‑default (FPD) modeling.
Experience mentoring junior data scientists or analysts.
Consult, client delivery, or solution‑oriented project experience.

Responsibilities

Lead custom analytics and modeling engagements from scoping through delivery and ongoing support.
Develop credit strategies and ML models (underwriting, line assignment, pricing, early warning, collections).
Engineer features from alternative, transactional, and bureau data (e.g., recency, frequency, volatility, trend, and behavioral metrics).
Evaluate and integrate third‑party/alternative data sources (sub‑prime bureaus, cash-flow, telco, utility, and specialty data).
Conduct segmentation, lift analysis, and champion/challenger testing to assess performance and incremental value.
Develop custom scorecards, policy rules, and ML models aligned with each client's risk appetite and regulatory requirements.
Partner with clients to build end‑to‑end credit strategies that balance approvals, losses, efficiency, and customer experience.
Deliver clear, executive‑ready insights, documentation, and strategy recommendations.
Present results directly to risk leaders, analytics teams, and senior client partners.
Support model implementation, monitoring, stability analysis, and ongoing optimization.
Work cross‑functionally with Product, Engineering, and Sales to align custom solutions with broader AFS capabilities.
Contribute to AFS best practices, reusable frameworks, and internal accelerators for non‑prime analytics.