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Abacus Insights is a healthcare data and AI platform serving health plans (payers). Backed by $100M in funding, the company helps health plans break down data silos, create trusted data foundations, and enable better decisions on outcomes, cost, and member/provider experience.
You will serve as a dedicated data and analytics partner working alongside business and client teams as Abacus launches new service offerings for health plans. This is a client-facing, individual-contributor analytics role focused on payment integrity, provider performance, and payer operations. You will translate client questions into data models, dashboards, and analyses that power recommendations, leveraging AI and automation to move faster and dig deeper.
You will travel onsite to client locations to gather requirements in person, present analysis directly to client stakeholders, and build trusted relationships. You are both the technical engine that makes insights possible, accurate, and repeatable, and a visible, credible voice in front of clients.
Day-to-day responsibilities include:
- Partner with Payment Integrity and Payer Business Domain consultants, translating client questions into data models, reports, and analyses
- Travel onsite to client locations to gather requirements, support live engagements, and build direct relationships with stakeholders
- Present data findings, dashboards, and analyses directly to client stakeholders, translating technical analysis into clear, actionable takeaways in real time
- Design and manage queries and data pipelines working directly in client Snowflake or Databricks environments, pulling from claims, pharmacy, and provider performance systems
- Perform deep-dive analyses on utilization trends, cost drivers, quality metrics, and provider performance; package findings into presentation-ready outputs
- Leverage AI and automation—from AI-assisted query generation to GenAI-powered insight summarization—to accelerate analysis and surface findings faster
- Ensure data accuracy, consistency, and technical rigor across all client-facing analytics deliverables
- Partner with Product, Engineering, and Data teams to scale, automate, and productize analytics workflows and tools
- Maintain working knowledge of payer domain concepts: claims, utilization management, quality measures, payment integrity, and provider contracting
The role offers unlimited paid time off, work-from-anywhere flexibility, comprehensive health coverage, equity for every employee, growth-focused environment, home office setup allowance, and monthly cell phone allowance.
REQUIREMENTS:
- 7–10+ years of hands-on experience in healthcare data analytics, gained at a payer, TPA, health plan vendor, or consulting firm serving payers
- Deep proficiency in SQL and Power BI and/or Tableau, with a track record of building production-grade dashboards and reporting tools
- Experience analyzing medical/pharmacy claims, provider performance, or payment integrity data
- Ability to translate ambiguous business questions into structured data models and clear analyses
- Strong client-facing communication and presentation skills; comfortable presenting analysis directly to client stakeholders and fielding questions in real time
- Comfort using AI-powered tools (e.g., Copilot-style assistants, GenAI for code/query generation, AI-enabled BI features) to work faster and surface insights
- Willingness and ability to travel onsite to client locations as part of a consulting engagement team
- Working familiarity with payer domain concepts: claims processing, utilization management, quality measures (HEDIS/STAR), payment integrity, or provider contracting
NICE-TO-HAVE:
- Experience with Python or another scripting language for data manipulation and automation
- Exposure to payment integrity specifically—claims auditing, fraud/waste/abuse detection
- Exposure to provider performance or network analytics
- Experience with cloud data warehouses (e.g., Snowflake, Databricks, Redshift, BigQuery)
- Master's degree in a quantitative field, Public Health, or a related discipline
