Note: The job is a remote job and is open to candidates in USA. Clearwave Corporation is an innovative healthcare technology company providing a patient-engagement platform for specialty practices. The Senior Data Analyst, Product will support product initiatives across Eligibility AI, Revenue Analysis, and Client Outcomes by developing metrics, dashboards, data models, and analyses that enable evidence-based prioritization and outcome measurement.
Responsibilities
- Analyze insurance eligibility and coverage data across the scheduling, pre-registration, and visit stages of the patient journey to track active coverage rate, plan identification accuracy, and eligibility flag clearance
- Monitor accuracy and performance of AI-driven eligibility checks (e.g., Code 42 non-response, flag resolution), and surface trends by payer, location, and client
- Partner with the Eligibility AI product manager to define and track success metrics before development begins, and report on outcomes (clean-claim impact, staff time saved, denial reduction) after launch
- Support revenue and opportunity modeling for initiatives, including attach-rate assumptions, unit economics, and renewal risk analysis
- Pull and validate Salesforce data (strategic renewals, new-logo run-rate) feeding the opportunity model, flagging data-mapping issues before they reach leadership reporting
- Help quantify and track initiative adoption and revenue contribution against goals set by the product and revenue teams
- Build and maintain recurring client-facing and internal outcome reporting (usage, adoption, satisfaction, business impact) across product areas, in the spirit of the outcome-measurement goals every PM on this team is held to
- Consolidate exports from the Clearwave Provider Portal and other internal sources into consistent, reusable data models so reporting doesn't have to be rebuilt from scratch each cycle
- Help Product Managers document ROI for completed initiatives (e.g., Payment at Pre-Reg, Voice AI, Reschedule Assist) using adoption rates, transaction/revenue volume, and customer satisfaction data
- Build and maintain dashboards and self-serve reporting that let Product Managers track their product area's P&L, feature usage, and client adoption without a one-off analysis request each time
- Translate ambiguous business questions from Product, CS, and leadership into clean data pulls, clear visualizations, and a defensible recommendation
- Maintain data quality and documentation for the metrics and definitions this team relies on, so numbers stay consistent across PMs and reporting cycles
- Participate in discovery and prioritization conversations to make sure decisions are grounded in usage and outcome data, not just anecdote
Skills
- 5+ years of experience in a data analyst, business analyst, or product analyst role, ideally supporting a product or SaaS organization
- 5+ years of experience and expertise in data structures, indexing, querying, and data retrieval concepts
- Experience in Big Data tools and languages (golang, python, spark)
- Strong SQL skills and comfort working directly with relational databases or a data warehouse
- Experience building dashboards and reports in a BI/visualization tool (e.g., Power BI, Tableau, Looker) or in spreadsheet-based tooling at scale
- Ability to turn a loosely defined business question into a structured analysis and communicate the findings clearly to non-technical stakeholders
- Comfort working with CRM data; Salesforce reporting experience preferred
- Bachelor's degree in a quantitative, business, or related field, or equivalent professional experience
- Experience in healthcare, health-tech, or another regulated data environment, with an understanding of handling sensitive data appropriately (HIPAA awareness)
- Familiarity with Python or R for data cleaning and analysis at scale
- Exposure to product analytics concepts (adoption funnels, cohort analysis, activation/retention metrics) and to empowered product-team operating models
- Experience working with EHR, PM system, or patient-scheduling data
- Exposure to streaming and batch data pipeline tooling (Kafka, AWS Glue or similar) and to large-scale transactional datasets (millions of records per day)
- Familiarity with AI/ML-driven analytics – evaluating model or rules-engine output (e.g., Drools), monitoring accuracy and drift, and translating AI performance into business metrics
- Experience supporting a platform migration (e.g., SQL Server to Snowflake, Power BI to Tableau) while maintaining reporting continuity
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