Forward Deployed AI-Enabled Analyst

Core Responsibilities

• Partner with product leadership and embedded client stakeholders to shape the analytics product roadmap for each engagement.
• Co-develop the value narrative for analytics products targeted at payers, providers, adjusters, and internal leadership audiences.
• Identify the high-impact clinical and operational metrics that anchor each product, including outcomes benchmarking, utilization trends, authorization efficiency, denial root cause, referral leakage, and network performance.
• Translate ambiguous business and clinical questions into product hypotheses, measurable KPIs, and prioritized backlog items.
• Conduct discovery with client and internal stakeholders to surface data gaps, unmet reporting needs, and emerging insight opportunities.
• Prioritize product initiatives using a structured framework aligned to platform roadmap and the client's strategic objectives.

Requirements Translation and Technical Specification

• Partner with product leadership to refine business requirements gathered from client engagements into actionable technical artifacts.
• Translate high-level requirements and metrics from PRDs into detailed technical specifications that engineering can build against on first review.
• Research and interpret requirements from regulatory bodies, including CMS, AHRQ, NCQA, and specialty registries, and embed them accurately into product builds.
• Document technical requirements with the precision that engineering teams need to estimate, build, test, and ship without ambiguity.
• Maintain requirements traceability from client problem statement through PRD, spec, build, and validation.

Data Modeling and Engineering Collaboration

• Produce data product specifications that data engineering can build directly against, including entity definitions, grain, lineage, and business rules.
• Collaborate with data engineering to validate model design, identify source system mappings, and define data quality requirements.
• Define semantic layer requirements, including metric definitions, hierarchies, and filters, for downstream BI tools and AI agents.
• Maintain a living data model catalog and analytics product dictionary as the reference for product, engineering, and client teams.

Data Assessment, Validation, and Quality Assurance

• Conduct thorough analyses of client data environments to assess data quality, completeness, and usability before and during product implementation.
• Verify availability and accessibility of required data elements across client source systems.
• Identify data gaps, inconsistencies, or quality issues that may impact implementation, and recommend remediation paths.
• Review and audit SQL logic developed by engineering teams to confirm alignment with product requirements and expected clinical or financial outcomes.
• Serve as the quality checkpoint between product specifications and technical delivery, partnering with engineering to troubleshoot discrepancies and optimize solutions.

AI Enablement and Workflow Integration

• Use AI tools throughout the discovery, specification, validation, and documentation lifecycle to accelerate work and improve accuracy.
• Support the evaluation and integration of AI agent capabilities, including data model generation, trend intelligence, and natural language analytics, into client workflows.
• Develop the prompts, evaluation criteria, and human-in-the-loop checkpoints that make AI-driven analytics trustworthy for clinical and financial decision making.
• Identify opportunities to replace manual reporting workflows with AI-augmented alternatives and quantify the impact.

Documentation and Stakeholder Communication

• Create clear, durable documentation that bridges business intent and technical implementation.
• Communicate technical findings and data insights to both technical and non-technical audiences, from data engineers to clinical executives.
• Represent credibly in client-facing forums, including working sessions, steering committees, and demos.

Required Qualifications

• 5+ years in analytics, product management, analytics consulting, or clinical informatics, with a meaningful portion in a healthcare context.
• Direct experience designing data models, including dimensional modeling, ERDs, and semantic layer definitions, in a healthcare or managed care environment.
• Strong SQL proficiency with demonstrated ability to write, read, and audit complex queries in modern cloud data warehouses such as Snowflake, BigQuery, or Databricks.
• Proven ability to write product and data requirements that engineers can act on, not just business narratives.
• Strong command of healthcare data constructs, including claims, authorizations, clinical episodes, utilization, denials, referrals, and outcomes.
• Experience working across payer, provider, or health plan stakeholder environments, with the maturity to navigate competing priorities.
• Excellent written and verbal communication, with the ability to translate fluidly between business and technical languages.

Pay: $30.00 - $50.00 per hour

Work Location: Remote