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.

• 8+ years of experience in data science and analytics, with a track record of driving strategy and impact across a business area (CRM, lifecycle marketing, growth, retention, or equivalent); consumer subscription, fintech, or large-scale owned-channel marketing experience strongly preferred.
• Demonstrated ability to apply first-principles thinking to translate ambiguous business strategy into analytical problems at the business-area level — framing the question before analysis runs, rather than waiting for one to be handed down.
• Proven success designing and interpreting complex experiments well beyond traditional A/B testing, and applying causal inference where experimentation is constrained (holdouts, quasi-experiments, incrementality, MMM or multi-touch attribution as inputs — not substitutes — for causal claims).
• Deep expertise in causal inference, customer segmentation, journey analytics, and experimentation design, with the judgment to balance statistical rigor and business considerations.
• Experience building and owning predictive models through their lifecycle (classification, regression, propensity, or similar) and being accountable for the decisions they inform, not just model accuracy.
• Experience creating reusable frameworks, methodologies, and data products that are adopted by a broader analytics and marketing community.
• Fluency in SQL and a statistical programming language (Python or R); experience partnering with engineering to harden pipelines, instrumentation, and production data products.
• Exceptional communication and stakeholder-influence skills, with a demonstrated ability to influence Director- and VP-level leaders across business and technical teams.
• Ability to navigate ambiguity with minimal guidance, make fast data-driven decisions (one-way vs. two-way door), and operate effectively in a fast-paced, seasonal business.
• Ability to use AI-native tools to plan, implement, and synthesize analyses across experiment readouts, quasi-experimental methods, revenue and retention deep dives, and measuring the success of journey or campaign launches. Comfort sizing AI use cases and evaluating non-deterministic systems, not only using AI as a personal productivity tool.
• BS or MS in Statistics, Mathematics, Operations Research, Computer Science, Engineering, Econometrics, or a related field (advanced degree preferred).

Nice to have

• Hands-on experience with CRM platforms (e.g., Braze) and owned-channel measurement (email, push, SMS, in-app).
• Experience designing journey-level measurement (behavioral milestones, coordinated intent) alongside campaign-level operational metrics.
• Familiarity with marketing mix models, multi-touch attribution, and when not to treat them as causal evidence.

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 $194,000 - $262,500