Senior Data Scientist - Customer Success

We are seeking a highly motivated and experienced Data Scientist to join our Customer Success Data Science team. In this role, you'll drive data-driven strategies to optimize Customer Success outcomes across our Accountants and Partner business, supporting the professionals and firms who serve small and mid-market businesses on Intuit's platform. You will frame the right question before the analysis runs and drive the evidence through to the decision that changes the business, owning the causal identification that isolates what actually moves outcomes. If you're passionate about shaping the future of customer success through data, we'd love to hear from you! Responsibilities

• Define KPIs and Success Metrics: Frame the right question before any analysis runs and establish the business metrics and causal levers the business manages to, such as onboarding and time-to-value, product adoption, customer health, retention, and growth, ensuring they are measured consistently across reporting surfaces and leadership reviews.
• Strategic Recommendations: Provide actionable recommendations using diverse data sets and business knowledge, even when complete data is unavailable, including a clear ship-or-iterate call when the model is imperfect.
• Experimentation and Causal Inference: Own the inference approach end to end. Choose and defend the method, whether a randomized experiment, a quasi-experiment, or a predictive model, and isolate what actually moved a metric, including for service and coverage investments where clean randomization is not feasible.
• Predictive Analytics and Modeling: Build and own predictive models such as customer health scoring, churn and expansion propensity, and next best action through their lifecycle, accountable for the outcomes they drive and not only their accuracy.
• Segmentation: Build and apply durable customer segmentation patterns to refine targeting, positioning, service coverage, and customer experience.
• Data Visualizations and Storytelling: Translate complex data into clear, accessible visualizations and author the executive-level communication that helps leaders understand the insight and act on it.
• Enable Self-Serve Analytics: Design, build, and run team data products that are automated, monitored, and reliable enough that the business reviews them each week without rebuilding them by hand. Work with Data Engineering to ensure data quality and enhance real-time analytic capabilities.
• AI Integration: Collaborate with AI teams to integrate AI solutions into business processes, size AI use cases against business value, and automate recurring analytical bottlenecks into agentic systems, owning the agent context so they inherit the team's rigor.
• Cross-Functional Collaboration: Partner with product, digital, customer support, sales, and marketing teams to identify opportunities, create data-driven strategies, and influence decision-making.

Qualifications

• 4+ years of experience working with product analytics, web analytics, customer care analytics, or other customer experience analytics, preferably in fintech
• Advanced proficiency in SQL, 'big data' technologies (e.g., Redshift, Spark, Hive, BigQuery), and BI tools (e.g., Tableau, Qlik, Plotly Dash)
• Deep expertise in experimentation design (A/B/n, bandits) and causal inference (Propensity Score, DiD, Synthetic Control) with a strategic understanding of their application, including on small-sample or non-randomized populations where clean control groups are hard to construct
• A track record of framing the question rather than only answering it: taking an ambiguous business problem through to a decision a team acted on
• Experience building and owning predictive models through their lifecycle, with accountability for the business outcomes they drive
• Experience building and maintaining data products that are automated, monitored, and reproducible rather than one-off analyses; directing AI tools to build and review that work is a plus
• Strong business acumen and the ability to translate business strategy into testable hypotheses and learning agendas
• Strong data storytelling skills, with a proven ability to rapidly construct impactful visualization, communicate insights and influence leadership
• Excellent communication and interpersonal skills, with a proven ability to build trust and collaborate seamlessly across technical, business, and cross-functional teams
• Comfortable working in a fast-paced environment and have flexibility to shift priorities when needed
• Bachelor's degree in Engineering, Data Science, Statistics, Mathematics, Computer Science, Economics or related quantitative field; Master's Degree preferred
• Preferred:
• Strong programming skills in Python or R; experience building ML and GenAI models, including automation and custom implementations
• Experience evaluating AI-native or non-deterministic systems, including defining what 'good' means when outputs vary
• Familiarity with the accounting, bookkeeping, or professional services domain, or an adjacent space such as tax
• Experience solving growth-related problems at financial technology companies serving consumer or SMBs

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. The expected base pay range for this position is: Mountain View $162,500 - $220,000