Senior Data Scientist - Customer Success

We are seeking a highly motivated and experienced Sr. 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