Principal Data Scientist, Payments

Small business owners trust Gusto with something fundamental: making sure their people get paid, on time, every time. As a Principal Data Scientist on the Payments team, you'll sit at the intersection of financial infrastructure and data science — translating the complexity of a high-scale payments ecosystem into the insights and models that shape how we build, operate, and improve our payments products.

In this role you will work closely with our Product, Engineering, Design, Finance, and other Data teams to become an expert in the data for your domain, define and track metrics that help us understand our business performance, and dive deep into our payments data to deliver insights and answer questions. You’ll also integrate AI-assisted practices to accelerate analysis, enhance rigor, and expand the reach of insights across Gusto.

This is a role for someone who's equally comfortable diving into ledger-level transaction data and standing in front of a payments engineering team to influence architecture decisions. If you want your work to directly protect and improve the financial lives of hundreds of thousands of small businesses, this is the role.

Here’s what you’ll do day-to-day:

• 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.

Here’s what we're looking for:

• 10+ years of experience in Data Science or a closely related quantitative role at a product-focused software company, with meaningful exposure to payments, fintech, or financial infrastructure.


• Deep payments domain knowledge: Hands-on experience with payments infrastructure: ACH, card networks, bank integrations, settlement flows, or equivalent, with a strong intuition for how backend systems shape data availability and quality.


• Strong SQL and Python skills, with experience querying large-scale transactional datasets and building reproducible analytical pipelines.


• Proven experimentation and causal inference expertise: Ability to design rigorous experiments in challenging payment contexts (rare events, high variance, non-stationarity) and communicate trade-offs clearly.


• AI-native mindset: Has built data products and analytical workflows with AI as a core component, not just used AI to go faster. Knows 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 scale AI use responsibly in a financial context.


• Cross-functional influence: Track record of collaborating with and influencing backend engineers and technical stakeholders, not just product and business partners.

Our cash compensation amount for this role is targeted at $184,000/yr - $210,000/yr in Denver, and $217,000/yr - $250,000 for San Francisco and New York. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above.