Data Scientist, AI Data Foundations

Data Scientist, AI Data Foundations About the Role Reporting into the Data Engineering organization, the Data Scientist is responsible for designing and building the curated data structures that AI and ML applications consume across MeridianLink. You will own the vector stores behind our RAG systems, the feature store that powers model training and inference, and the graph databases that capture relationships across applicants, products, and decisions. You will also lead targeted data discovery work, surfacing hidden trends in our lending and account-opening data that inform both AI use cases and the broader business. This is a hands-on, build-oriented role. You will not be primarily training large models — you will make sure the people training and serving models have high-quality, well-governed, well-engineered data to work with, and you will use your data science skills to validate that the data is fit for purpose. What You Will Do • Build and maintain vector stores for RAG: Design embedding pipelines, chunking strategies, indexing approaches, and refresh patterns for the vector stores powering retrieval-augmented generation across MeridianLink products. • Own the feature store: Design, build, and operate feature store assets used for model training and online/offline inference, including feature definitions, freshness SLAs, lineage, point-in-time correctness, and reuse across teams. • Design graph data structures: Build graph databases that model relationships between applicants, applications, products, lenders, decisions, and outcomes — and make them queryable for both AI use cases and analytical investigations. • Lead data discovery: Profile our lending, deposit, and behavioral datasets to identify hidden trends, segments, anomalies, and potential model drivers; turn findings into actionable hypotheses for product, risk, and growth teams. • Engineer for AI consumption: Build the curated, AI-ready datasets that downstream model builders, application engineers,