Data Scientist, AI Data Foundations

Become a Key Player as a Data Scientist AI Data Foundations

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.

Heres How Youll Make an Impact on the Team

• 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.

Heres What Youll Need to Be Successful in This Role

• 47 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.

Heres What Else Might Help You Out

• 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 ( faithfulness answer quality hallucination measurement).
• Exposure to graph algorithms (community detection link prediction centrality) applied to business problems.
• Bachelors or Masters in CS Statistics Mathematics Engineering or related quantitative field or equivalent experience.

Pay Range

$114000 - $175000/year

Ready to Make Your Mark

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

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