About the job
Intuit's Consumer Group is committed to building tools and services that improve our members' financial journeys. At the heart of this mission, the Credit Karma Engagement team builds in-product features and experiences that help members take meaningful action to improve their financial outcomes, from building credit, to saving money, to paying down debt.
The team is seeking a Sr. Staff Data Scientist to serve as an analytical leader & strategic thought partner to our Engagement product team, responsible for building value-driven features and experiences that drive long-term member retention. This is a high-impact, cross-initiative role where you will set the analytics vision, raise the scientific bar across the team, and influence product and customer lifecycle strategy across Credit Karma's business.
Responsibilities
Set data science strategy across Credit Karma's engagement and lifecycle product initiatives to evaluate the member lifecycle end to end, from activation and habit formation through retention, churn prevention, and reactivation.
Combine insights, business acumen, strategic considerations, and industry-wide learnings to influence cross-functional leaders up to the VP level; act as the connective tissue across Product, Marketing, Engineering, and Design.
Identify new ML and causal inference methodologies and external trends, adapt them to engagement, lifecycle, and churn-prevention use cases, and create shareable frameworks that enable adoption across the BU, with clarity on when and how each methodology should be used to drive business value.
Drive an iterative experimentation culture across the team by designing complex experiments (A/B/n, painted-door, bandits, and quasi-experimental designs) and applying causal inference (Propensity Score, DiD, Synthetic Control where A/B testing capability is limited).
Identify key patterns in member behavior by connecting insights across a portfolio of experiments and analyses; create durable member segmentation strategies that enhance targeting, personalization, and the in-product experience.
Co-create the analytics/AI strategy for engagement and lifecycle in partnership with cross-functional teams; guide phased testing and rollout with the right measurement, safety, risk, and ethical considerations; connect model performance metrics to member and business outcomes.
Mentor and elevate Data Scientists across the team, set scientific standards and best practices, contribute to calibrations and hiring, and scale yourself through delegation while remaining hands-on in the highest-leverage areas.
Qualifications
Minimum
9+ years of experience in data science and analytics, with a track record of driving strategy and impact across multiple initiatives or business units.
Demonstrated ability to apply first-principles thinking to translate ambiguous business strategy into analytical problems at the business-unit level.
Proven success designing and interpreting complex experiments well beyond traditional A/B testing, and applying causal inference where experimentation is constrained.
Deep expertise in causal inference, customer segmentation, and experimentation design, with the judgment to balance statistical rigor and business considerations.
Exposure to lightweight Machine Learning in terms of being able to build offline classification & regression models to inform business decisions.
Experience creating reusable frameworks, methodologies, and toolkits that are adopted by a broader analytics community.
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, dynamic environment.
Ability to use AI-native tools to plan, implement, and synthesize analyses across a variety of use cases ranging from experiment readouts, quasi-experimental methodology, retention/churn deep dives, and measuring the success of feature launches.
BS or MS in Statistics, Mathematics, Operations Research, Computer Science, Engineering, Econometrics, or a related field.
Preferred
Experience in consumer product engagement, retention, or growth strongly preferred.
Fintech experience a plus.
Experience in consumer platforms with a membership-based or multi-product ecosystem, where personalization and cross-product engagement drive long-term retention, is a plus.
Advanced degree preferred.