You will design and build the curated data structures that AI and ML applications consume, enabling higher-quality model training and inference. You will partner with model builders, product, risk, and growth stakeholders to surface actionable insights and ship production-ready vector, feature, and graph data assets. This is a Remote role.

• Build and maintain vector stores for RAG, including embedding pipelines, chunking strategies, indexing, and refresh patterns.
• Own the feature store: design, build, and operate feature definitions, freshness SLAs, lineage, and point-in-time correctness for offline/online use.
• Design and implement graph data structures to model relationships across applicants, applications, products, lenders, decisions, and outcomes.
• Lead data discovery: profile lending, deposit, and behavioral datasets to identify trends, segments, anomalies, and model drivers; produce actionable hypotheses for stakeholders.
• Engineer curated, AI-ready datasets with appropriate quality checks, documentation, and governance for downstream model builders and analysts.
• Define and run evaluation frameworks for RAG retrieval quality, feature drift, embedding quality, and graph completeness; iterate on metrics.
• Partner closely with ML engineers and applied scientists to ensure data assets accelerate model development and serving workflows.
• Champion responsible data use by collaborating with governance, security, and compliance teams to ensure data classification, consent, and regulatory boundaries are respected.
• Communicate findings via write-ups, notebooks, dashboards, and short presentations for technical and non-technical audiences.

• 4–7 years of experience in data science, ML engineering, or applied data roles, with significant time building data assets consumed by models or applications.
• Hands-on experience designing and operating vector stores for RAG or semantic search (embedding generation, chunking, indexing, retrieval evaluation).
• Experience building or operating a feature store (e.g., Databricks Feature Store, Feast, or custom), including offline training and online serving patterns and point-in-time correctness.
• Experience modeling and building graph data structures and writing graph queries (Neo4j, TigerGraph, Cosmos DB Gremlin, or similar).
• Strong proficiency in Python (pandas, NumPy, scikit-learn, PySpark) and SQL; comfortable using Databricks notebooks and jobs.
• Practical experience with embedding models and LLM tooling (Hugging Face, OpenAI/Azure OpenAI APIs, LangChain or similar) in production or near-production contexts.
• Demonstrated data discovery skills: profiling messy datasets, surfacing patterns, validating findings statistically, and explaining results clearly.
• Solid grounding in classical ML concepts (supervised vs. unsupervised learning, train/test discipline, leakage, evaluation metrics).
• Strong written and verbal communication skills for technical and business audiences.

• Experience in SaaS or FinTech, especially with lending, deposit, credit, fraud, or KYC/AML data.
• Familiarity with Databricks-native AI/ML tooling: Databricks Vector Search, Databricks Feature Store, MLflow, Unity Catalog.
• Experience with open-source vector DBs (pgvector, Pinecone, Weaviate, Chroma, FAISS) and strong opinions on trade-offs.
• Experience with Microsoft Azure data and AI services (Azure OpenAI, Azure AI Search, ADLS Gen2).
• Experience evaluating RAG systems end-to-end (recall@k, faithfulness, answer quality, hallucination measurement).
• Exposure to graph algorithms (community detection, link prediction, centrality) applied to business problems.
• Bachelor's or Master's in CS, Statistics, Mathematics, Engineering, or related quantitative field, or equivalent experience.

$114,000 - $175,000/year

This role may fill quickly. Submit your resume to be considered.

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