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

Madison, Mississippi, United States -

Full Time

Start Date

Immediate

Expiry Date

20 Mar, 26

Salary

239596.0

Posted On

20 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Machine Learning, Credit Risk Analytics, Python, SQL, Feature Engineering, Model Evaluation, Client Engagement, Financial Services, Alternative Data, Model Governance, Fraud Risk, Segmentation, Analytics, Consulting, Mentoring

Industry

Information Services

Description

Company Description Experian is a global data and technology company, powering opportunities for people and businesses around the world. We help to redefine lending practices, uncover and prevent fraud, simplify healthcare, create marketing solutions, and gain deeper insights into the automotive market, all using our unique combination of data, analytics and software. We also assist millions of people to realize their financial goals and help them save time and money. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more industry segments. We invest in people and new advanced technologies to unlock the power of data. As a FTSE 100 Index company listed on the London Stock Exchange (EXPN), we have a team of 22,500 people across 32 countries. Our corporate headquarters are in Dublin, Ireland. Learn more at experianplc.com. Job Description 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. Qualifications 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. 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. Additional Information Our uniqueness is that we celebrate yours. Experian's culture and people are important differentiators. We take our people agenda very seriously and focus on what matters; DEI, work/life balance, development, authenticity, collaboration, wellness, reward & recognition, volunteering... the list goes on. Experian's people first approach is award-winning; World's Best Workplaces™ 2024 (Fortune Top 25), Great Place To Work™ in 24 countries, and Glassdoor Best Places to Work 2024 to name a few. Check out Experian Life on social or our Careers Site to understand why. Experian is proud to be an Equal Opportunity and Affirmative Action employer. Innovation is an important part of Experian's DNA and practices, and our diverse workforce drives our success. Everyone can succeed at Experian and bring their whole self to work, irrespective of their gender, ethnicity, religion, colour, sexuality, physical ability or age. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity. Employee Status: Regular Role Type: Home Job Posting - Salary Range: $133,109 - $239,596 Department: Analytics Flexible Time Off: 20 Days Schedule: Full Time

Responsibilities

Lead custom analytics and modeling engagements, developing credit strategies and machine learning models for clients in non-prime lending. Deliver insights and recommendations to clients while supporting model implementation and optimization.