• Apoyar la implementación técnica de la estrategia institucional de datos. • Monitoreo, Evaluación y Aprendizaje (MEL) e Inteligencia Artificial de Fundación Capital. • Integración, procesamiento, análisis y visualización de datos. • Desarrollo de soluciones analíticas y casos de uso de IA que fortalezcan la medición de resultados, el aprendizaje institucional y la toma de decisiones.

Role Description The Data Verification Analyst is responsible for all bill review clerical functions, including mail, prepping and scanning medical bills, data entry, data verification and assisting with various phone and email tasks to support the bill review department. This is a remote position. Essential Functions & Responsibilities: - Validate incoming data to the Bill Review system - Identify and apply customer-specific rules and processes - Requires continual and consistent communication with supervisor regarding status of Data Verification queues and workload - Assist the bill review department with all clerical duties as assigned - Additional duties as assigned Qualifications - Ability to work on several concurrent tasks and prioritize workload with minimal direction - Ability to identify, analyze and solve problems - Basic computer proficiency, including familiarity with Microsoft Office applications - Strong interpersonal, time management, and organizational skills - Ability to work both independently and within a team environment Requirements - High school diploma or equivalent - Experience in a professional office environment preferred Benefits - Pay Range: $13.38 - $23.42 per hour - A comprehensive benefits package is available for full-time regular employees and includes: - Medical (HDHP) w/Pharmacy - Dental - Vision - Long Term Disability - Health Savings Account - Flexible Spending Account Options - Life Insurance - Accident Insurance - Critical Illness Insurance - Pre-paid Legal Insurance - Parking and Transit FSA accounts - 401K - ROTH 401K - Paid time off

• Own the analytics direction of the Data Platform — define the vision, standards, and roadmap for metrics, models, and reporting delivered through the platform layer. • Unify data models across product areas — work with the Platform Reports PM and all PMs to consolidate fragmented metric definitions into a single, governed semantic layer. • Drive merchant analytics success — design, validate, and continuously improve the reports and analytics every merchant relies on to study and grow their business. • Establish documentation standards — ensure every API, dataset, metric, and column is correctly named, clearly defined, and formally documented, so definitions are consistent from the warehouse to the merchant-facing report. • Enable growth analysis — build the analytical foundations (cohorts, funnels, growth metrics, benchmarks) that let merchants and internal teams study growth accurately. • Guard data quality and consistency — define validation rules, reconciliation checks, and metric governance so numbers match everywhere they appear. • Partner cross-functionally — act as the analytical authority for the platform layer, advising engineering, product, and other analytics teams on how to build against it correctly.

• Analyze player behavior across the full lifecycle, including acquisition, onboarding, engagement, retention, monetization, churn, and reactivation. • Identify opportunities and risks through player segmentation, funnel analysis, cohort analysis, retention analysis, and behavioral pattern analysis. • Design and evaluate A/B tests and other experiments, including hypothesis development, sample-size estimation, metric selection, statistical testing, and result interpretation. • Measure the performance of game features, promotions, events, bonuses, and campaigns. • Define and maintain key product metrics and analytical frameworks for different games and player segments. • Build dashboards and recurring reports that allow stakeholders to monitor product performance and make timely decisions. • Investigate unusual changes in KPIs and determine their underlying causes and business impact. • Translate complex analytical findings into clear recommendations for both technical and non-technical stakeholders. • Partner with Product Managers and game teams to define analytical requirements before launching new features or initiatives. • Work with Data Engineers to improve data quality, tracking coverage, data models, and analytical datasets. • Promote consistent analytical standards and encourage data-informed decision-making across the organization.