About Gusto
At Gusto, we're on a mission to grow the small business economy. We handle the hard stuff — payroll, health insurance, 401(k)s, and HR — so owners can focus on their craft and their customers. With teams in Denver, San Francisco, and New York, we support more than 500,000 small businesses nationwide and are building a workplace that reflects the people we serve.
All full-time employees receive competitive base pay, benefits, and equity (RSUs) — because everyone who helps build Gusto should share in its success. Offer amounts are determined by role, level, and location. Learn more about our Total Rewards philosophy.
AI is a fundamental part of how work gets done at Gusto. We expect all team members to actively engage with AI tools relevant to their role and grow their fluency as the technology evolves. AI experience requirements vary by role and will be assessed during the interview process.
About the Role
Gusto is looking for a highly skilled and motivated Staff Data Scientist to join our Time Products Data Science team. In this role, you'll leverage experimentation, statistical inference, and causal analysis to drive strategic decisions that shape how small businesses track their employees' time. The ideal candidate is a trusted data storyteller with strong statistical and coding skills, and a genuine passion for building products that make work better for employers and employees alike.
About the Team
You'll work closely with Product, Engineering, Design, and Finance partners embedded in our Time product teams — becoming the go-to data expert for your domain, defining and tracking metrics that reflect product health and customer outcomes, and surfacing insights that inform roadmap decisions. You'll also integrate AI-assisted practices to expand the reach and rigor of your analysis across the organization.
Here’s what you’ll do day-to-day
- Lead: Own ambiguous problems, design analysis frameworks, and introduce structure that scales across multiple product domains.
- Strategic Partnership: Collaborate with product managers, engineering leads, designers, and operations teams to proactively identify opportunities, align on strategy, and guide data-informed decision-making.
- Analytical Rigor: Apply advanced statistical methods, causal inference, experimentation, and AI-assisted analytics to surface drivers of product performance, separating signal from noise.
- Experimentation & Measurement: Design, analyze, and interpret experiments; ensure insights highlight trade-offs and limitations based on sample size and data quality.
- Execution: Deliver multiple high-impact projects, balancing trade-offs to maximize business value, and maintain clear expectations of deliverables and timelines.
- Communication: Present complex findings in a structured, compelling way to technical and non-technical stakeholders, fostering a data-informed mindset across the company.
- Independence: Work with minimal guidance to prioritize, create, and deliver data science roadmaps, proactively resolving conflicts or misalignment across stakeholders.
- Scaling the Craft: Mentor other data practitioners, up-level team best practices in experimentation, statistical modeling, and metric interpretation. Drive improvements in data quality, rigor, and adoption of better data capabilities leveraging AI-native tools and workflows across the org.
Here’s what we're looking for
- 7–10 years of experience in Data Science at a product-focused software company.
- Strong SQL skills and comfort with Python
- Proven ability to apply statistical methods, causal inference, AI tools, and experimental design to real business problems.
- Excellent communication skills, with a track record of influencing cross-functional stakeholders and leadership.
- Demonstrated experience leading large, technically complex projects with clear business impact.
- A proactive, resilient problem-solver who independently structures ambiguous problems into actionable insights.
- Passion for mentoring others and raising the bar for data science craft across the team.
- BS/MS/PhD in a quantitative field (Statistics, Economics, Computer Science, Applied Math, etc.) or equivalent industry experience.