Data Analyst

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Manila Recruitment

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2010

💼 Consulting

📦 Logistics

📣 Marketing

Consulting • Logistics • Marketing

Manila Recruitment is a leading recruitment agency based in the Philippines, specializing in innovative talent sourcing and headhunting services. The agency provides a wide range of recruitment solutions including executive search, IT recruitment, offshore staffing solutions, remote staffing, and more. With a focus on understanding clients' strategic business objectives, Manila Recruitment offers tailored recruitment strategies for multinationals, corporations, and start-ups entering the Filipino market. The firm prides itself on a process-driven approach, offering comprehensive candidate guarantees and a database of over 250,000 candidates. Certified headhunters at Manila Recruitment are dedicated to passive candidate sourcing, ensuring quality hires that align with company culture and requirements.

📋 Description

• 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 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 • 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 • 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

🎯 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 • 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