Back
Staff Data Scientist, Customer Success
Full-time
Overview 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 Pay—for small and mid-market customers.
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 execution across the organization.
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-loop experiences, and customer engagement models 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 the analytical bar of 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.
