Principal Data Scientist, Payments

• Own payments intelligence end-to-end: Design and maintain measurement frameworks for payment success rates, failure root causes, retry strategies, and settlement timing — becoming the authoritative source of truth for payments health across the org.
• Partner with payments engineering: Embed with backend engineering teams to understand infrastructure constraints and data availability, translate that knowledge into well-scoped experiments, and influence roadmap prioritization with data-backed recommendations.
• Drive experimentation at scale: Design and analyze A/B tests and quasi-experiments across payment flows — including retry logic, routing decisions, and failure recovery — ensuring statistical rigor even in low-conversion, high-variance payment environments.
• Build predictive models: Develop and deploy models for payment failure prediction, risk scoring, and anomaly detection that operate at Gusto's transaction volume, partnering with Machine Learning Engineers and Product Engineers on productionization and monitoring.
• Build AI-native data products: Go beyond using AI to go faster; build data products and automated workflows with AI as a core component. Know what "good" looks like for a clustering model or automated workflow, when to trust the output, when to challenge it, and how to build guardrails that let the team scale AI use responsibly in a regulated financial context.
• Own the business narrative: Be the person payments product and engineering leadership calls before an exec review, not after. Comfortable defending methodology on ARR projections, CX attribution, and cost savings in a room with skeptics. Translate nuanced statistical results and model outputs into clear, actionable narratives for payments engineers, product managers, finance partners, and executive leadership.

• Own payments intelligence end-to-end: Design and maintain measurement frameworks for payment success rates, failure root causes, retry strategies, and settlement timing — becoming the authoritative source of truth for payments health across the org.
• Partner with payments engineering: Embed with backend engineering teams to understand infrastructure constraints and data availability, translate that knowledge into well-scoped experiments, and influence roadmap prioritization with data-backed recommendations.
• Drive experimentation at scale: Design and analyze A/B tests and quasi-experiments across payment flows — including retry logic, routing decisions, and failure recovery — ensuring statistical rigor even in low-conversion, high-variance payment environments.
• Build predictive models: Develop and deploy models for payment failure prediction, risk scoring, and anomaly detection that operate at Gusto's transaction volume, partnering with Machine Learning Engineers and Product Engineers on productionization and monitoring.
• Build AI-native data products: Go beyond using AI to go faster; build data products and automated workflows with AI as a core component. Know what "good" looks like for a clustering model or automated workflow, when to trust the output, when to challenge it, and how to build guardrails that let the team scale AI use responsibly in a regulated financial context.
• Own the business narrative: Be the person payments product and engineering leadership calls before an exec review, not after. Comfortable defending methodology on ARR projections, CX attribution, and cost savings in a room with skeptics. Translate nuanced statistical results and model outputs into clear, actionable narratives for payments engineers, product managers, finance partners, and executive leadership.

• Own payments intelligence end-to-end: Design and maintain measurement frameworks for payment success rates, failure root causes, retry strategies, and settlement timing — becoming the authoritative source of truth for payments health across the org.
• Partner with payments engineering: Embed with backend engineering teams to understand infrastructure constraints and data availability, translate that knowledge into well-scoped experiments, and influence roadmap prioritization with data-backed recommendations.
• Drive experimentation at scale: Design and analyze A/B tests and quasi-experiments across payment flows — including retry logic, routing decisions, and failure recovery — ensuring statistical rigor even in low-conversion, high-variance payment environments.
• Build predictive models: Develop and deploy models for payment failure prediction, risk scoring, and anomaly detection that operate at Gusto's transaction volume, partnering with Machine Learning Engineers and Product Engineers on productionization and monitoring.
• Build AI-native data products: Go beyond using AI to go faster; build data products and automated workflows with AI as a core component. Know what "good" looks like for a clustering model or automated workflow, when to trust the output, when to challenge it, and how to build guardrails that let the team scale AI use responsibly in a regulated financial context.
• Own the business narrative: Be the person payments product and engineering leadership calls before an exec review, not after. Comfortable defending methodology on ARR projections, CX attribution, and cost savings in a room with skeptics. Translate nuanced statistical results and model outputs into clear, actionable narratives for payments engineers, product managers, finance partners, and executive leadership.

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