Data Scientist, AI Data Foundations

🕒 Maio 6

🇺🇸 Estados Unidos – Remoto (EUA)

💵 $114.593 - $195.400 / ano

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

📊 Cientista de Dados

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MeridianLink

SiteLinkedInTodas as Vagas

501 - 1000 funcionários

Fundada em 1998

💳 Fintech

🏦 Bancário

☁️ SaaS

💰 $485.000.000 Post-IPO Debt em 2021-11

Fintech • Banking • SaaS

A MeridianLink é uma fornecedora líder de soluções SaaS para instituições financeiras, especializada em sistemas de originação de empréstimos e tecnologias de transformação digital. Sua plataforma fim-a-fim melhora as experiências digitais através da integração com sistemas de gestão de empréstimos hipotecários (LOS), soluções para abertura de contas de depósito e mais. Os sistemas em nuvem da MeridianLink aprimoram a eficiência no processamento de empréstimos e cobranças, na tomada de decisões orientadas por dados e na gestão de contas. A empresa colabora com parceiros para expandir seu alcance de mercado e impulsionar o crescimento na indústria de fintech. Com mais de 25 anos de experiência, a MeridianLink é dedicada a apoiar bancos, cooperativas de crédito e outros provedores de serviços financeiros através de tecnologia e inteligência empresarial.

Descrição

• 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, and analysts rely on — with appropriate quality, documentation, and governance baked in. • Evaluate retrieval and feature quality: Define and run evaluation frameworks for RAG retrieval quality, feature drift, embedding quality, and graph completeness; iterate based on what the metrics tell you. • Partner with model builders: Work closely with ML engineers and applied scientists to make sure the data structures you build accelerate their work rather than slow it down. • Champion responsible data use: Partner with governance, security, and compliance to ensure that AI-facing data assets respect data classification, customer consent, and regulatory boundaries from day one. • Communicate findings: Translate discovery work into clear narratives — write-ups, notebooks, dashboards, and short presentations — that help non-technical stakeholders act on what the data is showing.

🎯 Requisitos

• 4–7 years of experience in a data science, ML engineering, or applied data role, with a meaningful portion of that time spent building data assets that other people's models or applications consumed. • Hands-on experience designing and operating vector stores for RAG or semantic search, including embedding generation, chunking, indexing, and retrieval evaluation. • Experience building or operating a feature store (e.g., Databricks Feature Store, Feast, or a custom internal platform), including offline training and online serving patterns and point-in-time correctness. • Experience modeling and building graph data structures using Neo4j, TigerGraph, Azure Cosmos DB Gremlin, or similar graph databases — and writing graph queries to answer real questions. • Strong proficiency in Python (pandas, NumPy, scikit-learn, PySpark) and SQL; comfortable working day-to-day in Databricks notebooks and jobs. • Practical experience with embedding models and LLM tooling (e.g., Hugging Face transformers, OpenAI / Azure OpenAI APIs, LangChain or similar) in a production or near-production context. • Demonstrated data discovery skills: profiling messy real-world datasets, surfacing non-obvious patterns, validating findings statistically, and explaining them clearly. • Solid grounding in classical ML concepts — supervised vs. unsupervised learning, train/test discipline, leakage, evaluation metrics — even though you will not own model training day-to-day. • Strong written and verbal communication skills; able to write up findings for both technical and business audiences.

🏖️ Benefícios

• Insurance coverage (medical, dental, vision, life, and disability) • Flexible paid time off • Paid holidays • 401(k) plan with company match • Remote work