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: RemoteEmployee Location: Los Angeles, CaliforniaJob Type: Contract, with the potential for an extension and/or full-time employmentContract Length: 5 weeks to startContract Start Date: The week of October 20, 2025Responsibilities: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 inputsCreate and maintain APIs for analytics, reporting, and system integrations (HubSpot, Jira, Slack)Build and test logic for automated alerts, recommendations, and system responsesCollaborate with AI and product teams to ensure data models align with real-world use casesExample 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 resultsAPI Endpoints: Implement /tiers/:tier/metrics, /events/:id, /fixpacks/:id/effects with RBACIngestion Classification Logic: Implement prompt classifier (BOS/CDS/IES) in pipelineAutomation Hooks: Add BOS crisis alerts → HubSpot/Slack; CDS/IES tasks → JiraQualifications: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 fieldStrong understanding of data modelling, graph traversal, and API developmentExperience with Postgres, RESTful APIs, and Python or Node.jsFamiliarity with automation and workflow tools (e.g., Slack, Jira, HubSpot)Bonus:Experience with AI systems or knowledge representation frameworksUnderstanding of data lineage, observability, and automated governance