Data Analyst

As a Data Analyst, you will build the client's analytical foundation from the ground up by establishing a single source of truth for business metrics, developing reliable dashboards for leadership and department heads, and delivering actionable insights that drive data-informed decision-making across customer retention, acquisition, operations, and product development.

This is a high-impact, foundational role that will shape the company's retention strategy, sales performance, physician recruitment, operational planning, and ability to define, measure, and achieve meaningful OKRs. You will work closely with the Head of Business Operations, the CEO, and cross-functional leaders across Customer Success, Sales, Marketing, Physician Experience, and Operations.

Duties and Responsibilities:

Dashboards & Reporting

• Build, maintain, and continuously improve executive and operational dashboards using available data sources including BigQuery, Google Analytics, Odoo, Abacus AI, and others
• Develop a central metrics layer that integrates data across marketing, sales, customer success, and physician experience into a coherent, single source of truth
• Ensure all key business metrics — including churn rate, LTV, conversion rates, cohort performance, revenue per client, and physician supply/demand by state — are tracked in one accessible, well-maintained place
• Deliver regular reporting cadences to leadership with clear visualizations, trend commentary, and recommended actions
• Build automated alerts and monitoring so leadership is notified when key metrics move outside expected ranges — reducing reliance on manual review
• Leverage Abacus AI and other AI-powered tools to accelerate analysis, surface patterns, and build predictive models that would otherwise require significant manual effort

Churn & Retention Analysis

• Analyze churn and retention patterns across client cohorts to identify when clients are most at risk and what behavioral or operational factors predict cancellation
• Build and maintain survival cohort models to understand client lifetime value and the inflection points at which clients tend to stabilize or disengage
• Surface early warning signals in client behavior — such as frequency of invoice review, support ticket volume, platform inactivity, or communication cadence — to help Customer Success intervene proactively rather than reactively
• Evaluate the effectiveness of retention initiatives, discount strategies, and re-engagement programs with rigorous before/after analysis
• Model the revenue impact of reducing churn by specific percentage points to help leadership prioritize retention investments

Conversion & Growth Analysis

• Analyze conversion funnels across the clients platform and website — from page views to registrations, intro calls booked, and active subscriptions — identifying where qualified prospects drop off
• Break down conversion performance by physician listing attributes, source channel, device type, and registration path to identify high-leverage optimization opportunities
• Measure the impact of product changes — such as blurred listing features, rate changes, profile enhancements, or UI updates — on registration and conversion rates
• Support Marketing with analytics from Google Analytics and BigQuery to assess traffic quality, campaign performance, and the distinction between qualified and unqualified demand
• As new behavioral tracking tools are introduced (e.g., PostHog or similar session-level analytics), take ownership of analyzing individual-level engagement data and connecting it to downstream outcomes

Client & Physician Insights

• Develop and maintain structured client and physician persona profiles based on actual platform data — including demographics, specialty, state, practice type, rate sensitivity, and behavioral patterns
• Analyze physician supply and demand by state, specialty, and availability to support the Physician Experience team in identifying where to focus recruitment efforts for maximum business impact
• Investigate the characteristics of successful versus unsuccessful collaboration relationships and translate findings into actionable guidance for Customer Success, Sales, and the matching process
• Support the development of a more sophisticated matching intelligence layer as clients scales — identifying data signals that predict strong, durable collaboration relationships

Operations Team Support

• Partner with the Operations Team — who oversees Customer Service Representatives handling inbound calls, subscription management, and billing — to identify data needs and build analytical support tailored to their workflows
• Analyze inbound call volume, inquiry types, and resolution patterns to surface trends that can improve operational efficiency and service quality
• Track and report on subscription activity — new activations, cancellations, and payment failures — providing the Operations Team with timely visibility into the health of the subscriber base
• Build billing and collections analytics to monitor invoice aging, dispute frequency, and payment behavior, helping the team proactively manage accounts before issues escalate
• Identify recurring operational bottlenecks — such as high-volume inquiry categories or common billing friction points — and translate findings into process improvement recommendations
• Support the Operations Team in establishing KPIs and reporting cadences so that Customer Service performance is tracked consistently and objectively

OKR & KPI Support

• Partner with department heads to establish reliable baseline metrics that ground quarterly OKR-setting in real data rather than estimates
• Create and maintain a shared KPI scorecard for leadership meetings, ensuring all metrics are consistently defined, sourced from the same data, and comparable over time
• Flag and resolve discrepancies in how different teams calculate the same metrics (e.g., differing churn rate definitions) and drive alignment on a single authoritative methodology
• Build the reporting infrastructure that enables leadership to track OKR progress in real time rather than waiting for end-of-quarter reviews

Data Infrastructure & Tooling

• Collaborate with the CEO and technical team to improve the underlying data infrastructure — ensuring data from the platform, CRM, billing system, website, and marketing tools flows into a reliable, queryable layer
• Document data definitions, metric methodologies, and dashboard logic so that the analytical work is transparent, reproducible, and not dependent on any one individual
• Evaluate and recommend new data tools and integrations as the company scales, balancing capability with cost and complexity
• As client grows, help shape what a more mature data function could look like — potentially including additional analysts, data engineers, or specialized roles

Ad Hoc Analysis & Strategic Projects

• Respond to leadership requests for one-off analyses as business questions emerge — from pricing strategy to geographic expansion to investment preparation
• Contribute analytical support to cross-functional initiatives such as product launches, sales team changes, or retention program redesigns
• Explore new data sources and analytical approaches as they become available, bringing a continuous improvement mindset to the function

Requirements

Must-have Skills / Qualification:

• At least 3+ years of experience in a data analyst or business intelligence role, ideally in a SaaS, marketplace, subscription, or technology company
• Proficiency in SQL for querying, transforming, and analyzing data from relational databases and data warehouses
• Experience building dashboards and data visualizations in tools such as Google Looker Studio, Tableau, Power BI, or similar
• Hands-on experience with Google Analytics and Google BigQuery, or equivalent data warehouse and web analytics platforms
• Strong analytical thinking with the ability to translate complex, ambiguous data into clear, concise business insights and recommendations
• Excellent English communication skills — written and verbal — with the ability to present findings to non-technical stakeholders including senior leadership
• Self-starter with strong ownership mentality; comfortable working independently in a lean, fast-moving startup environment where data may be incomplete or imperfect
• Curiosity and business acumen — you don't just answer the question asked, you ask what question should be asked

Advantageous or Nice-to-Have Skills/Experience:

• Experience with cohort analysis, LTV modeling, churn/retention analysis, or other subscription business analytics
• Familiarity with Abacus AI or other AI-powered analytics and business intelligence platforms
• Exposure to product analytics or behavioral tracking tools such as PostHog, Mixpanel, or Amplitude
• Familiarity with CRM or ERP platforms; experience with Odoo, Salesforce, HubSpot, or similar is a plus
• Experience with marketing analytics platforms — Google Ads, Meta Ads, or similar — is a plus
• Understanding of US healthcare concepts (e.g., collaborative practice, NP/PA scope of practice, telehealth) is an advantage but not required
• Comfort with or curiosity about AI/ML techniques — regression, classification, clustering — and how they can be applied to business analytics problems
• Experience working in a marketplace, two-sided platform, or healthcare technology company is a plus