Data Analyst (Fully Remote) - #35221

Manila Recruitment

10d•Remote

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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
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
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 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
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
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
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
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
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