Principal Data Scientist - Data Asset Evaluation & Strategic Partnership

Experian

2d•Remote

About The Position

We are looking for an experienced Principal Data Scientist to evaluate new data assets, including M&A targets, strategic partners, and third‑party data providers across the credit lifecycle. You will sit at the intersection of data science, product strategy, and corporate development, rigorously assessing the predictive power, stability, scalability, and regulatory viability of external datasets. You'll partner with Product, Corporate Development, Legal, Risk, and external counterparties. You will report to the VP of Analytics Product Build, Innovation, and Scores. This role is fully remote. You'll have opportunity to: Evaluate traditional, alternative, transactional, and raw datasets for use in underwriting, portfolio management, collections, and fraud. Lead quantitative due diligence for M&A targets and data partnerships, assessing data quality, depth, coverage, stability, and scalability. Design and implement validation frameworks to measure predictive lift, segmentation value, and incremental performance versus incumbent data. Conduct benchmarking and champion/challenger analyses comparing external data assets with internal attributes, scores, and models. Engineer consumer, account, or business-level features from raw or event-level data, especially for early-stage data providers. Develop and test feature construction methods (recency, frequency, velocity, volatility, trend, and stability) to evaluate modeling potential. Assess data assets across the full credit lifecycle—acquisition, underwriting, account management, early warning, and loss mitigation. Translate analytical findings into investment theses, valuation inputs, and go/no-go recommendations for Product and Corporate Development. Evaluate regulatory and compliance considerations: explainability, permissible purpose, adverse action suitability, data provenance, and governance. Partner with Legal and Privacy teams to assess consent, permissible use, data rights, and regulatory risks. Build repeatable toolkits, scorecards, and dashboards to standardize how data assets are evaluated. Lead technical deep dives and data reviews with external data providers, fintechs, and potential acquisition targets. Present findings to senior partners through executive-ready materials that communicates risk, value, integration effort, and strategic fit. Support post‑acquisition or post‑partnership integration through guidance on feature pipelines, monitoring strategies, and performance tracking.

Requirements

5+ years of experience in data science, credit risk analytics, or advanced analytics within financial services, FinTech, or data-driven platforms.
Hands-on experience transforming raw transactional, event-level, or unstructured data into model-ready features.
Proficiency in Python (Pandas, NumPy, SciPy, scikit‑learn, SQLAlchemy) for feature engineering, validation, and analysis.
Advanced SQL experience with large, multi-source datasets.
Experience with credit risk metrics and model evaluation (AUC, KS, lift, PSI, stability, and back‑testing).
Experience designing incremental value tests, challenger analyses, and controlled experiments.
Summarize complex analytical outcomes into clear, defensible business recommendations.
Comfortable presenting in high‑visibility, decision-oriented environments.
Experience collaborating across Product, Risk, Legal, Compliance, and Strategy teams.
Experience supporting M&A due diligence, data acquisitions, or strategic partnership evaluations.
Familiarity with fair lending expectations, model explainability, and regulatory compliance for new data usage.
Experience evaluating early-stage fintechs or data-as-a-service providers with developing data products.
Exposure to ML models used for underwriting, fraud, or early-warning systems.
Experience building standardized evaluation frameworks or internal analytics guides.
Understanding of data commercialization and productization considerations.

Responsibilities

Evaluate traditional, alternative, transactional, and raw datasets for use in underwriting, portfolio management, collections, and fraud.
Lead quantitative due diligence for M&A targets and data partnerships, assessing data quality, depth, coverage, stability, and scalability.
Design and implement validation frameworks to measure predictive lift, segmentation value, and incremental performance versus incumbent data.
Conduct benchmarking and champion/challenger analyses comparing external data assets with internal attributes, scores, and models.
Engineer consumer, account, or business-level features from raw or event-level data, especially for early-stage data providers.
Develop and test feature construction methods (recency, frequency, velocity, volatility, trend, and stability) to evaluate modeling potential.
Assess data assets across the full credit lifecycle—acquisition, underwriting, account management, early warning, and loss mitigation.
Translate analytical findings into investment theses, valuation inputs, and go/no-go recommendations for Product and Corporate Development.
Evaluate regulatory and compliance considerations: explainability, permissible purpose, adverse action suitability, data provenance, and governance.
Partner with Legal and Privacy teams to assess consent, permissible use, data rights, and regulatory risks.
Build repeatable toolkits, scorecards, and dashboards to standardize how data assets are evaluated.
Lead technical deep dives and data reviews with external data providers, fintechs, and potential acquisition targets.
Present findings to senior partners through executive-ready materials that communicates risk, value, integration effort, and strategic fit.
Support post‑acquisition or post‑partnership integration through guidance on feature pipelines, monitoring strategies, and performance tracking.