Data Analyst

Description We are a profitable healthcare technology company on a mission to transform and build the operating system for post-acute care. Each year, millions of seniors transition out of hospitals into skilled nursing facilities, home health, or back home — and far too often, that handoff fails them: readmissions, medication errors, missed follow-ups, families left in the dark. Our Anna™ platform sits at the seams of that journey, giving providers, payers, and caregivers the visibility and tools to get the right care to the right person at the right time. Through clinical intelligence, deep datasets, and connected technology, Anna™ empowers providers and payers to collaborate to deliver exceptional outcomes, streamline care coordination, and enhance patient experience. PAA currently partners with seven of the top ten health systems, most national health plans, and national and regional post-acute providers. What makes PAA distinctive isn't the funding or the growth curve — it's how we work. Autonomy and ownership are non-negotiable. AI amplifies expertise, it doesn't replace it. User trust is infrastructure, not a metric. The Data Analyst is embedded within PAA's operations organization to bring analytical rigor to how our clients are performing on the Anna™ platform and what's actually driving the trends we see. You'll work day-to-day alongside Client Success and the broader Operations team — those accountable for delivering on our clients' contractual goals — turning large and varied datasets into the answers those teams need to act on. Your work spans macro and micro. On any given day you might be building a book-of-business performance view for a Quarterly Business Review (QBR), unpacking why a specific client's utilization trend shifted last quarter, or surfacing network expansion opportunities from referral flow patterns across post-acute providers. You'll be the person the operational leaders come to when they need to understand what the data is saying — and, over time, the person who sees the pattern first. This role reports into Operations but has matrixed support from the VP, Engineering & Analytics — pairing the operational context that makes your work actionable with the analytical mentorship and standards to keep sharpening your craft. Client Performance Analytics

• Analyze how each client is performing against contractual goals, KPIs, and operational benchmarks on the Anna™ platform
• Build and maintain reporting views that give Client Success leaders and account owners a clear, current picture of book-of-business health
• Surface leading indicators of client risk or opportunity before they become escalations
• Support QBR, executive review, and internal client success planning cycles with reliable, decision-ready analytics

Trend & Root Cause Analysis

• Investigate the drivers behind client performance trends — utilization shifts, workflow changes, provider network dynamics, seasonality, product usage patterns
• Move from "what happened" to "why" using cohort analysis, comparative benchmarking, and appropriate statistical methods
• Translate findings into clear narratives — charts, memos, briefings — that operational leaders and clients can act on without a translator

Network & Opportunity Analytics

• Support broader Operations with data-driven insight into provider network coverage, capacity, and integration health across the SNF, home health, and broader post-acute footprint
• Identify macro-level opportunities (market gaps, network expansion priorities, integration friction patterns) and micro-level opportunities (specific facility outliers, workflow bottlenecks, data quality issues)

Data Craft & Practice

• Work with large, varied, and sometimes messy datasets — Anna platform data, claims, EHR/EMR extracts, client-provided files — and bring rigor to how they're structured, joined, validated, and interpreted
• Build reusable data assets (queries, models, dashboards, notebooks) that scale beyond a single analysis and outlast a single request
• Partner with the VP, Analytics on analytical standards, methods, documentation, and reusable frameworks; contribute back to the broader analytics function

Requirements

• 3–5 years of hands-on data analyst experience with proven fluency working across large and varied datasets
• Strong background / experience with SQL. Writing complex queries against large relational datasets comfortably, including joins, window functions, CTEs, and performance-aware patterns
• Working proficiency in Python or R for analysis (ex. Pandas, Jupyter notebooks, basic statistical methods)
• Data visualization capability in Tableau, Power BI, Looker, or comparable. Building views to inform executive decision-making
• Solid grasp of analytical fundamentals: cohorting, benchmarking, distributions, basic statistical inference; enough judgment to know when to reach for what • Advanced Excel for ad hoc work and executive-facing summaries
• Ability to translate messy operational questions into clean analytical problems, and analytical output into clear narratives non-analysts can act on
• Bachelor's degree in a quantitative or analytical discipline (data science, statistics, economics, mathematics, engineering, public health, informatics, or comparable)

Preferred

• Experience with healthcare data — claims, EHR/EMR data, HL7/FHIR standards, or utilization and case management data
• Familiarity with post-acute care, managed care, Medicare Advantage, or value-based care contexts
• Prior experience embedded within an operational or client-facing team, not only centralized analytics or BI
• Working familiarity with clinical outcomes concepts — readmissions, LOS, ED utilization, total cost of care

Compensation — Competitive base salary, annual performance bonus, and equity participation commensurate with experience. Benefits — Comprehensive medical, dental, and vision. 401(k) with employer match. Effective the 1st of the month after start date. Flexibility — Remote or hybrid. Flexible PTO. 10 paid holidays. Frequent team offsites. Growth — Matrixed mentorship from the VP, Analytics, direct exposure to operational leadership, a learning & development budget, and real influence on how PAA uses data.