Join Intuit’s TurboTax CRM and Lifecycle Marketing data science team and help shift CRM from campaign-level optimization to customer journey design. In this Staff Data Scientist role (onsite in Mountain View, CA), you will use causal measurement, experimentation, and AI-native analytics to improve retention, product engagement, and long-term customer value.
Pay range: USD 194,000 - 262,500 per year. This position may be eligible for a cash bonus, equity rewards, and benefits, in accordance with Intuit’s applicable plans and programs.
What you’ll do
- Drive Lifecycle Marketing strategy through data by framing the right questions before analysis is requested.
- Turn ambiguous marketing and product problems into analytical frameworks, metric trees, and testable hypotheses that can reshape 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 define success at each customer journey stage: re-engagement and start, then completion, attach, and return the following year.
- Apply causal measurement and counterfactual reasoning to determine what actually moved a metric, including campaign incrementality, journey-level lift, channel mix, halo, and retention, and translate results into the decisions business areas make.
- Design and run experiments (plus quasi-experiments, holdouts, and synthetic tests) when a clean A/B is not available.
- Act as a strategic data science partner across Marketing, Product, Data Engineering, Finance, and adjacent growth teams such as paid acquisition, Credit Karma, and in-product messaging.
- Translate complex findings into clear recommendations for Director- and VP-level stakeholders and drive outcomes through to decision, whether via Slack, readouts, models, or working sessions.
- Run deep-dive analyses on customer journeys, audience segments, funnel and cohort performance, and lifetime value to identify where CRM has the most leverage.
- Design segmentation and personalization strategies that improve targeting, reduce wasted volume, and reserve messaging capacity for high-value journeys.
- Create dashboards, visualizations, and self-serve tools, including GenAI-powered applications, so teams can act quickly.
- Build AI-native measurement, data products, and agentic systems: identify and prioritize CRM AI use cases (personalization, journey orchestration, insight generation), and evaluate them using frameworks such as golden datasets, LLM-as-judge with human review, and synthetic tests to certify non-deterministic experiences.
- Build and maintain data products the business area and its agents depend on by prototyping the data model, pipeline, and surface, then partnering with engineering to harden what sticks.
- Automate recurring analytical bottlenecks into trusted agentic systems with encoded business logic, monitoring, and clear guidance on what can run without a human in the loop.
- Champion data hygiene, instrumentation, and decision governance across CRM reporting and campaign measurement.
What you bring
- 8+ years of data science and analytics experience, with a track record 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 is strongly preferred.
- First-principles thinking to translate ambiguous business strategy into analytical problems at the business-area level, framing the question before analysis runs.
- Proven success designing and interpreting complex experiments beyond traditional A/B testing, with causal inference when experimentation is constrained (using inputs like holdouts, quasi-experiments, incrementality, MMM, or multi-touch attribution for causal claims).
- Deep expertise in causal inference, customer segmentation, journey analytics, and experimentation design, with judgment to balance statistical rigor and business needs.
- Experience building and owning predictive models through their lifecycle (classification, regression, propensity, or similar) and being accountable for the decisions they inform.
- Experience creating reusable frameworks, methodologies, and data products adopted by a broader analytics and marketing community.
- Fluency in SQL and a statistical programming language (Python or R), plus experience partnering with engineering to harden pipelines, instrumentation, and production data products.
- Exceptional communication and stakeholder influence, with 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, and operate effectively in a fast-paced, seasonal business.
- Comfort using AI-native tools to plan, implement, and synthesize analyses across experiment readouts, quasi-experimental methods, revenue and retention deep dives, and measuring journey and campaign launch success.
- Comfort sizing AI use cases and evaluating non-deterministic systems, not only using AI as personal productivity.
- BS or MS in Statistics, Mathematics, Operations Research, Computer Science, Engineering, Econometrics, or a related field (advanced degree preferred).
Technologies: SQL, Python, R, GenAI, LLM-as-judge, A/B testing, MMM, multi-touch attribution.
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.
