Neo4j Engineer / Applied Data Scientist

We’re looking for a computer scientist or back-end engineer with deep experience in Neo4j and graph-based systems to help extend and operationalize our data intelligence infrastructure. You’ll work on projects that connect cross-customer insights, detect emerging patterns, and power recommendation and readiness tools for AI-driven decision-making. Workplace Type: Remote Employee Location: Los Angeles, California Job Type: Contract, with the potential for an extension and/or full-time employment Contract Length: 5 weeks to start Contract Start Date: The week of October 20, 2025 Responsibilities: • Extend and optimize the Neo4j schema to support complex relational data (tiers, metrics, classifications, event nodes, etc.) • Develop ingestion and classification pipelines that automatically tag and route new data inputs • Create and maintain APIs for analytics, reporting, and system integrations (HubSpot, Jira, Slack) • Build and test logic for automated alerts, recommendations, and system responses • Collaborate with AI and product teams to ensure data models align with real-world use cases Example Tasks: • Extend Neo4j Schema: Add nodes/edges for Tier → QueryCohort → Metrics (SoP, ∆SoP, SSI, PDI, CCI) • Postgres Fact Tables: Create daily partitioned tables for tiered metrics and results • API Endpoints: Implement /tiers/:tier/metrics, /events/:id, /fixpacks/:id/effects with RBAC • Ingestion Classification Logic: Implement prompt classifier (BOS/CDS/IES) in pipeline • Automation Hooks: Add BOS crisis alerts → HubSpot/Slack; CDS/IES tasks → Jira Qualifications: • 3+ years of hands-on experience with Neo4j (Cypher queries, schema design, performance optimization) • Bachelor’s or Master’s in Computer Science, Data Science, or related field • Strong understanding of data modelling, graph traversal, and API development • Experience with Postgres, RESTful APIs, and Python or Node.js • Familiarity with automation and workflow tools (e.g., Slack, Jira, HubSpot) Bonus: • Experience with AI systems or knowledge representation frameworks • Understanding of data lineage, observability, and automated governance