Senior Data Analyst, Product Optimization - Healthcare RCM/Insurance Discover

FinThrive

About The Position

As a Senior Data Analyst on the Product Optimization team, you will turn complex healthcare revenue cycle data into evidence-based product decisions for FinThrive’s Insurance Discover product. This role is designed for someone who is deeply technical, statistically curious, and comfortable working across very large datasets to identify root causes, quantify product performance, and recommend measurable improvements. You will partner with Product Management, Technology, Operations, Finance, Sales, and customer-facing teams to uncover data quality issues, coverage discovery gaps, operational inefficiencies, and opportunity areas that directly affect customer outcomes and recovered revenue. The ideal candidate can move fluidly from SQL and Databricks analysis to executive-ready storytelling, translating technical findings into practical recommendations and prioritized action plans.

Requirements

Bachelor’s degree in Statistics, Data Science, Information Systems, Computer Science, Mathematics, Economics, Engineering, Healthcare Informatics, or another quantitative / technical field; advanced degree preferred but not required.
6+ years of experience in data analytics, product analytics, business analytics, healthcare analytics, revenue cycle analytics, or a related analytical role.
Healthcare revenue cycle experience, ideally with exposure to eligibility, coverage discovery, claims, denials, patient access, payer data, provider workflows, or healthcare data exchange.
Advanced SQL skills, including complex joins, CTEs, window functions, aggregation logic, data validation, performance-aware querying, and analysis across large, messy datasets.
Hands-on experience with big-data environments or cloud data platforms such as Databricks, Snowflake, Azure Data Lake, Synapse, Spark, or comparable tools.
Strong statistical and analytical foundation, with the ability to apply practical methods such as cohort analysis, segmentation, variance analysis, trend analysis, outlier detection, correlation, sampling, confidence-based reasoning, or experiment / impact measurement.
Technical fluency with data pipelines, data models, data quality concepts, APIs / data exchange patterns, and the practical realities of working with operational healthcare data.
Experience using AI-enabled tools or analytics approaches to improve productivity, identify patterns, summarize findings, document analysis, or support decision-making; must be comfortable experimenting with responsible AI use cases.
Proficiency in data visualization and storytelling using Tableau, Power BI, SSRS, Excel, or similar tools, with the ability to create leadership-ready insights rather than simply reporting numbers.
Ability to communicate clearly with both technical and non-technical stakeholders, including translating complex findings into concise recommendations, risks, tradeoffs, and next steps.
Strong problem-solving, critical thinking, prioritization, and project management skills; ability to drive multiple initiatives with minimal supervision in a collaborative, matrixed environment.

Nice To Haves

Experience supporting product optimization, product operations, product performance management, or data-driven product strategy in a healthcare technology environment.
Working knowledge of payer / provider data, EHR or practice management workflows, eligibility transactions, insurance discovery, coverage verification, or revenue recovery processes.
Practical Python or PySpark experience for data manipulation, statistical exploration, automation, or repeatable analytical workflows.
Experience designing analytical QA checks, data reconciliation logic, exception reporting, or repeatable monitoring frameworks for operational data products.
Familiarity with model-assisted analysis, machine learning concepts, predictive analytics, or experimentation frameworks; ability to partner with data science or engineering teams when deeper modeling is required.
Comfort operating in ambiguous problem spaces where the first task is to define the problem, locate the data, validate assumptions, and create a structured path to action.

Responsibilities

Develop and maintain deep subject matter expertise in Insurance Discover, including product lifecycle, customer workflows, eligibility / coverage discovery logic, data ingestion, matching behavior, operational processes, and the value chain required to deliver customer outcomes.
Analyze large, complex healthcare revenue cycle datasets to identify trends, gaps, anomalies, failure patterns, data quality issues, and opportunities to improve product accuracy, performance, scalability, and customer impact.
Use advanced SQL and big-data tooling such as Databricks to query, structure, profile, and interpret high-volume datasets across multiple sources and operational workflows.
Apply descriptive, diagnostic, and basic statistical techniques to assess product performance, validate hypotheses, measure impact, and distinguish signal from noise in complex data environments.
Leverage AI-assisted analytics, automation, and emerging AI capabilities to accelerate pattern detection, anomaly identification, root-cause exploration, documentation, and insight generation while maintaining strong quality controls.
Build metrics, dashboards, scorecards, and analytical frameworks that measure the effectiveness of product enhancements, operational optimizations, and customer-facing outcomes.
Translate technical analysis into clear recommendations, business cases, decision support, and executive-ready narratives for Product, Technology, Operations, Finance, Sales, and leadership stakeholders.
Partner with cross-functional teams to prioritize improvements, define success measures, support implementation, and monitor whether changes deliver the intended business and customer outcomes.
Support high-impact initiatives related to data integrity, coverage discovery performance, integration workflows, reporting accuracy, process optimization, and product stabilization / enhancement efforts.
Become a trusted analytical partner who can challenge assumptions, frame ambiguous problems, and bring structure to complex data questions in a fast-paced, matrixed environment.