Data Scientist - Gusto ; k

About Gusto

At Gusto, we're on a mission to grow the small business economy. We handle the hard stuff—like payroll, health insurance, 401(k) s, and HR—so owners can focus on their craft and customers. With teams in Denver, San Francisco, and New York, we’re proud to support more than 400,000 small businesses across the country, and we’re building a workplace that represents and celebrates the customers we serve.

Learn more about our Total Rewards philosophy.

As a Senior/Staff Data Scientist supporting Gusto Retirement, you will play a crucial role in leveraging experimentation, statistical inference, and causal analysis to drive strategic decision making that contributes to the overall success of our organization. The ideal candidate is a trusted data storyteller with strong statistical and coding skills, and a passion for applying these skills to support the retirement financial platform product for Gusto’s small business customers.

In this role, you will work closely with our cross-functional 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 Gusto Retirement 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.

• Lead: Own ambiguous problems with an AI-native approach, 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, and experimentation to surface drivers of product performance, separating signal from noise.
• Experimentation & Measurement: Design, analyze, and interpret experiments; leverage AI tools and workflows to 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.
• Operate with Agility: Drive progress with an adaptive and creative mindset to navigate and succeed in a dynamic environment where technologies and project scopes are constantly evolving.
• Scaling the Craft: Mentor other data practitioners, uplevel team best practices in experimentation, statistical modeling, and metric interpretation. Drive improvements in data quality, rigor, and adoption of better data capabilities across the org.

• 5 – 10 years of experience in Data Science at a product-focused software company
• Tactical SQL skills and experience with Python
• Must be comfortable learning new tools and pivoting quickly. Demonstrated comfort with ambiguity and an adaptive mindset in a constantly changing work environment
• Tangible experience integrating efficient AI-assisted practices to accelerate analysis, enhance rigor, and expand the reach of insights
• Proven ability to apply statistical methods, causal inference, and A/B testing 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

Our cash compensation amount for this role is $135,320 to $189,900 annually in Denver & most major metro locations, and $163,830 to $202,050 annually for San Francisco & New York. Final offer amounts are determined by multiple factors including candidate location, experience and expertise and…