Principal Data Scientist - Data Asset Evaluation & Strategic Partnership

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

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 counter parties.

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.

• 5+ years of experience in data science, credit risk analytics, or advanced analytics within financial services, Fin Tech, or data‑driven platforms.
• Hands‑on experience transforming raw transactional, event‑level, or unstructured data into model‑ready features.
• Proficiency in Python (Pandas, Num Py, Sci Py, scikit‑learn, SQL Alchemy) 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,…