Intuit is hiring a Senior Data Scientist for the Customer Success Data Science team in Mountain View, CA (onsite). In this role, you’ll help shape data-driven strategies that improve onboarding, adoption, customer health, retention, and growth outcomes. You’ll work at the intersection of causal inference, predictive modeling, experimentation, and analytics product enablement, partnering across product, support, sales, digital, and marketing to influence real business decisions.
What you’ll do
- Define KPIs and Success Metrics: Frame the right question upfront, set business metrics and causal levers the business manages (including onboarding and time-to-value, product adoption, customer health, retention, and growth), and ensure consistent measurement across reporting surfaces and leadership reviews.
- Deliver strategic recommendations: Use diverse datasets and business context to recommend next steps even when data is incomplete, including a clear ship-or-iterate decision when model performance is imperfect.
- Lead experimentation and causal inference: Own the inference approach end to end, choosing and defending methods such as randomized experiments, quasi-experiments, or predictive modeling, and isolate what actually moved metrics even for service and coverage investments where clean randomization is not feasible.
- Build predictive analytics with outcome accountability: Create and own models such as customer health scoring, churn and expansion propensity, and next best action across the customer lifecycle, with responsibility for outcomes driven rather than accuracy alone.
- Create durable segmentation: Build and apply customer segmentation patterns that improve targeting, positioning, service coverage, and customer experience.
- Turn analysis into executive action: Produce clear visualizations and author executive-level communications that help leaders understand insights and act on them.
- Enable self-serve analytics: Design, build, and run automated, monitored, reliable team data products so business teams review results weekly without manual rebuilding, partnering with Data Engineering to ensure data quality and enhance real-time analytic capabilities.
- Integrate AI into workflows: 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, including ownership of agent context so they inherit team rigor.
- Collaborate cross-functionally: Work with product, digital, customer support, sales, and marketing teams to identify opportunities, create data-driven strategies, and influence decision-making.
What you bring
- 4+ years of experience in product analytics, web analytics, customer care analytics, or other customer experience analytics, ideally 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), including applying these methods in small-sample or non-randomized settings where clean controls are difficult
- A track record of framing the question through to decisions the team acts on, not only answering it
- Experience building and owning predictive models through their lifecycle with accountability for business outcomes
- Experience building and maintaining automated, monitored, reproducible data products (one-off analyses are not the goal); directing AI tools to build and review that work is a plus
- Strong business acumen and the ability to translate strategy into testable hypotheses and learning agendas
- Strong data storytelling skills with the ability to rapidly create impactful visualizations and influence leadership
- Excellent communication and interpersonal skills, building trust across technical and cross-functional teams
- Comfort working in a fast-paced environment with flexibility to shift priorities
- Bachelor’s degree in Engineering, Data Science, Statistics, Mathematics, Computer Science, Economics, or a related quantitative field; Master’s Degree preferred
Tools you’ll use
SQL, Redshift, Spark, Hive, BigQuery, Tableau, Qlik, Plotly Dash, Python, R
Nice to have
- Strong programming skills in Python or R, including experience building ML and GenAI models, automation, and custom implementations
- Experience evaluating AI-native or non-deterministic systems, including defining what “good” means when outputs vary
- Familiarity with accounting, bookkeeping, or professional services (or adjacent areas such as tax)
- Experience solving growth-related problems at financial technology companies serving consumer or SMBs
Compensation: USD 162,500 - 220,000 per year.
