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Flywire is seeking a Data Science Manager to lead the newly created Payment Performance & Insights team. You will serve as the technical anchor, working directly with the VP and payment leadership to bridge complex transactional data and strategic executive decisions.
This is a full-stack analytical role requiring expertise across the entire data lifecycle. You will architect and maintain efficient data products using dbt, SQL, and Looker; design semantic layers to establish single sources of truth for KPIs; and leverage Gen AI tools to accelerate data discovery and pipeline optimization.
Key responsibilities include:
**Data Foundations & Modeling**: Architect and build reliable data products (dbt models, pipelines, Looker explores) enabling data-driven decision-making. Partner with Flywire's centralized Data & Analytics team to build core payment data assets, consolidate sources of truth, and eliminate fragmented business logic. Design semantic layers where key business KPIs are defined once, governed clearly, and consumed consistently across dashboards and models. Leverage Gen AI and modern analytics tooling to accelerate data discovery, automate documentation, and optimize pipeline maintenance.
**Experimentation & Testing**: Define and own Flywire's core payment experimentation methodology, setting standards for sample sizing, statistical power, and significance. Partner with Monetization and Experience Product teams to design, implement, and interpret A/B tests on pricing, FX rates, and payer behavior. Apply advanced statistical techniques to extract signal from noise in highly skewed data.
**Anomaly Detection & Monitoring**: Develop alerting models to spot anomalies in FX rates, margin shifts, volume mix shifts, card authorization rates, and cost structures. Build AI-powered automated early warning systems that monitor transactional health across thousands of corridors, scaling defense systems beyond manual analysis.
**Strategic Analysis & Insights**: Partner closely with product, engineering, and business to understand challenges and translate ambiguous business questions into actionable insights. Link user conversion and drop-off patterns in the payment flow to actual margin impacts that direct product improvement priorities. Bring proactive and influential product leadership into areas where data-driven decisions are essential.
Flywire is a global payments enablement and software company supporting over 5,300 clients across education, healthcare, travel, and B2B industries, processing payments across 240 countries and territories in 140+ currencies. The company has 1,400+ employees across 15 global offices.
**Requirements:**
- Bachelor's degree in Statistics, Mathematics, Data Science, Economics, Computer Science, or related quantitative field
- 6+ years of experience applying advanced analytical methods to large-scale data, preferably in fast-paced fintech or tech environments
- Advanced proficiency in SQL (specifically GoogleSQL/BigQuery) and Python
- Proven experience developing and maintaining production-level data transformation pipelines using dbt (Data Build Tool)
- Experience building and maintaining backend semantic models in LookML (Looker)
- Solid practical understanding of statistics, hypothesis testing, A/B test design, and experimental methodologies
- Exceptional business acumen and product sense with proven ability to translate strategic questions into analytical frameworks and recommendations
- Collaboration mindset; excited to be a technical thought partner within a lean team while serving as strategic thought partner to payment leadership
**Preferred Qualifications:**
- Direct domain experience in fintech, especially global digital payments, merchant acquiring, card schemes, or multi-currency FX platforms
- Deep familiarity with Google Cloud Platform (GCP) data ecosystem (BigQuery, Looker, Vertex AI/GCP GenAI tooling)
- Experience applying advanced statistical techniques (causal inference, propensity score matching) to highly skewed transactional datasets
- Experience using Generative AI tools to accelerate coding workflows, data exploration, and predictive models
