Staff Data Scientist, CRM & Lifecycle Marketing

Come join the TurboTax CRM and Lifecycle Marketing data science team as a Staff Data Scientist. We design how Intuit engages customers across owned channels to drive retention, product engagement, and long-term customer value. Our mission is to accelerate decision-making through advanced models, causal measurement, and experimentation that address our most important customer and business challenges. This role will be pivotal in shifting CRM from campaign-level optimization to customer journey design: defining the metrics and causal levers the business manages to, designing learning plans that resolve the highest-stakes decisions, and building the measurement and data products the lifecycle marketing organization depends on. You will operate as a senior individual contributor, partnering closely with Marketing, Product, Engineering, and Tax leadership to identify, validate, and refine strategies that move customers through behavioral milestones — not just messages — and compound into retention and lifetime value.

The ideal candidate thrives at the intersection of lifecycle marketing, causal inference, and AI-native analytics. You frame the right question before any analysis runs, with a command of the domain that others check their own framing against. This is a high-impact role where your work will directly influence how TurboTax invests in CRM, which journeys get built, and how we know they are working.

Responsibilities Drive Lifecycle Marketing Strategy through Data — Frame the right question self-directed, before analysis is requested. Translate ambiguous marketing and product problems into analytical frameworks, metric trees, and testable hypotheses that alter the CRM roadmap and resource decisions. Partner with Tax leadership and the Lifecycle Marketing team to define north-star metrics, align on learning plans, and establish what success looks like at each stage of the customer journey — from re-engagement and start through completion, attach, and return the following year. Causal Measurement that Changes Business Decisions — Apply causal inference and counterfactual reasoning to isolate what actually moved a metric — campaign incrementality, journey-level lift, channel mix, halo, and retention — and change the business-area decision it feeds. Design and run experiments, quasi-experiments, holdouts, and synthetic tests when a clean A/B is not available. Cross-Functional Influence — Serve as the strategic data science partner to leaders across Marketing, Product, Data Engineering, Finance, and adjacent growth teams (paid acquisition, Credit Karma, in-product messaging). Translate complex analytical findings into clear recommendations for Director- and VP-level stakeholders, and drive the work through to the decision in whatever medium lands — slack, readout, model, or working session. Insights, Journeys & Customer Value at Scale — Conduct deep-dive analyses on customer journeys, audience segments, funnel and cohort performance, and lifetime value to inform where CRM has the most leverage. Design segmentation and personalization strategies that improve targeting, reduce wasted volume, and free messaging capacity for high-value journeys. Create dashboards, visualizations, and self-serve tools — including GenAI-powered applications — so marketing and leadership can act quickly.

AI-Native Measurement, Data Products & Agentic Systems — Identify, size, and prioritize AI use cases for CRM (personalization, journey orchestration, insight generation) against business value, and apply evaluation frameworks (golden datasets, LLM-as-judge with human review, synthetic tests) so non-deterministic experiences can be certified rather than shipped on vibes. Build and maintain the data products the business area and its agents depend on: prototype the data model, pipeline, and surface, then partner with engineering to harden what sticks. Automate recurring analytical bottlenecks into trusted agentic systems that stakeholders can run independently, with encoded business logic, monitoring, and a clear call on what may run without a human in the loop. Champion data hygiene, instrumentation, and decision governance across CRM reporting and campaign measurement.

Qualifications The ideal candidate is a curious, self-directed data scientist with experience building scalable solutions, a deep understanding of customer behavior, and the judgment to balance statistical rigor with business speed.