Senior Product Data Scientist, Risk

Gusto, Inc.•San Francisco, CA

4d•Hybrid

About The Position

As a Senior Data Scientist, 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 help small businesses thrive. In this role you’ll be working with an established team and risk leaders in Engineering, Product, Design, Operation, Identity and Compliance. You'll define and track metrics that help us understand our business performance, and dive deep into our risk stack 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.

Requirements

5-7 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, and experimental design to real business problems.
Excellent communication skills, with a track record of influencing cross-functional stakeholders and leadership.
Demonstrated experience leading 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.

Nice To Haves

AI experience requirements vary by role and will be assessed during the interview process.

Responsibilities

Partner and Execute: Translate ambiguous stakeholder questions into clear, testable analyses. Structure complex business problems, identify drivers of performance, and communicate actionable insights to influence product or business decisions.
Analytical Depth: Use sound statistical reasoning and experimentation frameworks to separate signal from noise. Select appropriate analytical techniques that balance rigor and speed, and leverage AI-assisted analytics to surface drivers of product performance, separating signal from noise.
Experimentation & Measurement: Design and interpret experiments within your domain. Work with stakeholders to define success metrics, measure impact, and communicate trade-offs.
Communication: Tell clear, data-driven stories that connect findings to business strategy. Adapt communication for technical and non-technical audiences.
Execution: Own end-to-end delivery of analyses and metrics for your product area. Collaborate with cross-functional peers to scope, prioritize, and deliver insights that inform decisions.