Staff Data Scientist, CRM & Lifecycle Marketing

Overview 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