GBSG Customer Success - Staff Data Scientist

The GBSG Customer Success Data Science & Analytics team's mission is to empower world-class customer experiences across digital and expert-led channels by delivering data-driven insights, experimentation, and predictive intelligence. We partner closely with Customer Success, Product, Data Engineering, and Operations teams to improve customer engagement, retention, and long-term success across Intuit's Services businesses—Payroll,Payments, and Bill Payfor small andmid-marketcustomers.

As a Staff Data Scientist supporting Services, you will serve as a senior individual contributor and strategic thought partner, applying deep analytical expertise and business judgment to some of GBSG CS's most complex and high-impact problems. You will shape measurement frameworks for expert-led and human-assisted success motions, lead advanced experimentation and causal analysis, and translate insights into clear recommendations that influence strategy and executionacross theorganization.

This role offers a unique opportunity to help define how Customer Success and expert-services impact is measured at scale—especially as Intuit evolves its data platforms, AI-enabled and human-in-the-loopexperiences,and customer engagementmodels across the Services portfolio.

Responsibilities

• Serve as a strategic analytics partner to Customer Success, Services, Product, and Operations leaders—helping define problems, success metrics, and data-informed decisions across Payroll, Payments, and Bill Pay.
• Conceptualize ambiguous business problems, formulate hypotheses, and design rigorous analytical approaches to evaluate Customer Success and expert-services programs (e.g., CSM coverage, outbound and inbound-friction motions, 'Nail the Basics').
• Design, execute, and interpret experiments beyond traditional A/B testing, including causal inference methods (e.g., quasi-experiments, DiD, matching, synthetic control) to isolate the incremental impact of human-assisted success motions.
• Develop and maintain scalable measurement frameworks for key CS and Services outcomes such as engagement, retention, share-of-wallet, TPV growth, customer health, and support effectiveness.
• Build predictive models and durable customer segmentation approaches to improve targeting, prioritization, and CSM assignment across the Services customer base.
• Size and prioritize customer-friction and revenue-risk opportunities, turning analysis into clear, actionable roadmaps for Services leadership.
• Apply modern AI tooling to accelerate the analytics workflow—using LLM-assisted development environments (e.g., Cursor, Claude) and internal AI/MCP capabilities to move faster from question to insight, while holding a high bar for correctness and reproducibility.
• Design and evaluate AI/ML- and LLM-powered customer experiences, building the measurement and causal frameworks that determine whether agentic and human-in-the-loop success motions actually move customer and business outcomes.
• Translate complex analyses into clear, actionable insights and narratives for both technical and non-technical stakeholders, including senior leadership and cross-functional Services partners.
• Partner with Data Engineering to ensure high data quality, well-defined metrics, and scalable analytics assets—especially during platform and data migrations.
• Champion analytics rigor, experimentation best practices, and reusable solutions that scale impact beyond individual projects.
• Role-model Intuit's 'Win Together' mindset by collaborating deeply across teams and elevating theanalyticalbarof the broader organization.

Qualifications

• 8+ years in data science, analytics, or product analytics, with demonstrated impact in customer success, product, or go-to-market domains.
• Deep foundation in advanced analytics: causal inference and quasi-experimental design (DiD, matching, synthetic control, regression discontinuity), statistical modeling, and experimentation well beyond simple A/B testing—including knowing which method fits an ambiguous, real-world business question and defending the choice.
• Predictive modeling and segmentation experience—building models (propensity, churn/retention, LTV, customer health) that are used in production decisions, not just one-off analyses.
• Advanced SQL and strong Python for analysis, modeling, and experimentation (pandas, numpy, scikit-learn, statsmodels).
• Working fluency with modern AI tooling for data science—using LLM-based coding assistants (e.g., Cursor, Claude) and AI/agent workflows to increase speed and quality, with sound judgment about where AI accelerates the work and where human rigor must own the result.
• Proven ability to work with large, complex datasets and translate insights into business decisions.
• Excellent communication and storytelling skills, with the ability to influence senior stakeholders.
• Bachelor's degree in a quantitative field (Statistics, Economics, Mathematics, Computer Science, Data Science, or related); advanced degree preferred.

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.
Mountain View $194,000 - $262,500