Why This Job is Featured on The SaaS Jobs
Risk has become a defining product surface area for many subscription platforms, particularly those serving small businesses where trust, identity, and compliance directly shape adoption. This Senior Data Scientist, Risk role stands out because it sits at the intersection of product analytics and platform integrity, partnering with engineering and risk leadership to turn messy, high-stakes signals into decisions that affect the core customer experience.
For a SaaS data science career, this kind of remit builds durable skills: designing experiments where outcomes are constrained by safety and policy, applying causal thinking to operational systems, and translating statistical results into product and business trade-offs. The emphasis on metrics, inference, and clear storytelling reflects the reality of modern SaaS teams where analytics is expected to guide strategy, not just report performance. Exposure to AI-assisted analysis also signals an environment where tooling and workflow innovation are part of the craft.
This role is best suited to someone who enjoys ambiguity, can move between deep technical work and executive-ready narratives, and prefers cross-functional influence over isolated modeling. It will fit a senior practitioner who wants ownership of end-to-end analytical work in a domain where rigor and judgment matter as much as speed.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
About the Role:
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.
Here’s what you’ll do day-to-day:
- 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.
Here’s what we're looking for:
- 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.
Our cash compensation amount for this role is targeted at $133,000/yr - $159,000/yr in Denver, and $162,000/yr - $193,000 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.
