Staff Data Scientist - Time Products

Gusto

San Francisco (CA)

On-site

USD 190,000 - 230,000

Full time

14 days+

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Job summary

Gusto is seeking a Staff Data Scientist in San Francisco to leverage statistical inference and causal analysis to drive product decisions. You'll collaborate with cross-functional partners to apply advanced analytics in a fast-paced environment. The ideal candidate will have 7–10 years of experience in Data Science, strong SQL and Python skills, and the ability to influence stakeholders through excellent communication. Competitive salary range is $190,000–$230,000 per year, with a hybrid work model expected.

Qualifications

  • 7–10 years of experience in Data Science at a product-focused software company.
  • Proven ability to apply statistical methods, causal inference, and experimental design.
  • Excellent communication skills with a track record of influencing stakeholders.

Responsibilities

  • Own ambiguous problems and design analysis frameworks.
  • Collaborate with product managers and engineering leads to identify opportunities.
  • Apply advanced statistical methods and analyze experiments.

Skills

SQL
Python
Statistical methods
Causal inference
AI tools
Communication skills
Mentoring

Education

BS/MS/PhD in a quantitative field

Job description

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.

About the Role

Staff Data Scientist in the Core Products Data Science team. Leverage experimentation, statistical inference, and causal analysis to drive strategic decisions that shape how small businesses manage their benefits and track employee time. Trusted data storyteller with strong statistical and coding skills, passionate about building products that make work better for employers and employees.

About the Team

Work closely with Product, Engineering, Design, and Finance partners embedded in our Benefits and Time product teams. Become the go‑to data expert for your domain, define and track metrics that reflect product health and customer outcomes, and surface insights that inform roadmap decisions. Integrate AI‑assisted practices to expand the reach and rigor of analysis across the organization.

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.
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.
Compensation and Location

Target salary ranges: $155,000–$189,000 / yr in Denver; $190,000–$230,000 / yr in San Francisco and New York. Final offers are based on candidate experience and expertise. Gusto has physical office spaces in Denver, San Francisco, and New York City. Employees located in those cities are expected to work from the office 2–3 days per week (or more depending on role). Hybrid employees must maintain a secure and reliable internet connection on non‑office days.

EEO Statement

Gusto is an equal‑opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic. We consider qualified applicants with criminal histories consistent with applicable law and provide reasonable accommodations for individuals with disabilities and disabled veterans. If you require a medical or religious accommodation during your candidate journey, please complete the form and a member of our team will contact you.

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