Staff Data Scientist - Engagement

Intuit, Inc.; Charlotte (NC); On-site; USD 170,000 - 210,000; Full time

Job summary

Intuit is seeking a Staff Data Scientist for the Credit Karma Engagement team to partner with the Subscriptions product team and use data to improve the member experience across the lifecycle, from trial to renewal.

You will apply ML, causal inference, and advanced analytics; design experiments, build segmentation, mentor teammates, and influence roadmaps with Product and Engineering partners in a fast-paced fintech environment.

Qualifications

• 7+ years of experience in data science and analytics, with a track record of driving impact within a product area or initiative; experience with subscription, membership, or lifecycle-based products strongly preferred, fintech experience a plus.
• Ability to translate ambiguous product questions into structured analyses and actionable recommendations.
• Experience designing and interpreting experiments, with working knowledge of causal inference techniques for use when experimentation is constrained.
• Solid understanding of 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 contributing to reusable analytical frameworks, methodologies, or tools used by others on the team.
• Strong communication and stakeholder-influence skills, with the ability to work effectively with Director-level partners across business and technical teams.
• Ability to navigate ambiguity, make fast data-driven decisions, and operate effectively in a fast-paced, dynamic environment.
• Ability to use AI-native tools to plan, implement, and synthesize analyses across use cases ranging from experiment readouts to retention/churn deep dives.

Responsibilities

• Drive analytics strategy for Subscriptions:Own the analytics agenda for the Subscriptions product area, mapping the member journey end to end, from trial and onboarding through conversion, renewal, and cancellation, to identify the highest-leverage opportunities.
• Partner with product leadership:Combine data insights with business acumen to partner with Product, Engineering, and Design leaders in shaping roadmap and prioritization decisions.
• Apply advanced analytical methods:Apply ML and causal inference techniques to subscriptions-specific problems such as churn prediction, renewal optimization, and pricing or packaging experiments, adapting existing frameworks or building new ones as needed.
• Design and run experiments:Design and execute experiments (A/B/n, and quasi-experimental designs where testing is constrained) to evaluate changes to the member experience and quantify their impact on member and business outcomes.
• Build member segmentation:Analyze member behavior within Subscriptions to build segmentation that improves targeting, personalization, and offer strategy.
• Contribute to the AI-native roadmap:Partner with the team to identify opportunities to apply AI/ML within the subscriptions experience, help design measurement plans, and connect model performance to member and business outcomes.
• Mentor and support the team:Mentor other data scientists on best practices and experimentation rigor, and contribute to hiring and calibration as needed.

Skills

Data science

Experiment design

Causal inference

Customer segmentation

Offline ML models

Communication

Stakeholder influence

Ambiguity tolerance

Education

BS or MS in Statistics, Mathematics, Operations Research, Computer Science, Engineering, Econometrics, or related field

Job description

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 Staff Data Scientist to serve as an analytical partner to our Subscriptions product team, responsible for using data to improve the member experience across the subscription lifecycle, from trial and conversion through renewal and retention. This is a high-impact role where you will bring scientific rigor to product decisions, partner closely with Product and Engineering, and help shape how we measure and grow subscription value for our members.

Responsibilities

• Drive analytics strategy for Subscriptions:Own the analytics agenda for the Subscriptions product area, mapping the member journey end to end, from trial and onboarding through conversion, renewal, and cancellation, to identify the highest-leverage opportunities.
• Partner with product leadership:Combine data insights with business acumen to partner with Product, Engineering, and Design leaders in shaping roadmap and prioritization decisions.
• Apply advanced analytical methods:Apply ML and causal inference techniques to subscriptions-specific problems such as churn prediction, renewal optimization, and pricing or packaging experiments, adapting existing frameworks or building new ones as needed.
• Design and run experiments:Design and execute experiments (A/B/n, and quasi-experimental designs where testing is constrained) to evaluate changes to the member experience and quantify their impact on member and business outcomes.
• Build member segmentation:Analyze member behavior within Subscriptions to build segmentation that improves targeting, personalization, and offer strategy.
• Contribute to the AI-native roadmap:Partner with the team to identify opportunities to apply AI/ML within the subscriptions experience, help design measurement plans, and connect model performance to member and business outcomes.
• Mentor and support the team:Mentor other data scientists on best practices and experimentation rigor, and contribute to hiring and calibration as needed.

Qualifications

We're looking for a curious, proactive data scientist with a passion for driving member engagement and retention.

• 7+ years of experience in data science and analytics, with a track record of driving impact within a product area or initiative; experience with subscription, membership, or lifecycle-based products strongly preferred, fintech experience a plus.
• Ability to translate ambiguous product questions into structured analyses and actionable recommendations.
• Experience designing and interpreting experiments, with working knowledge of causal inference techniques for use when experimentation is constrained.
• Solid understanding of 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 contributing to reusable analytical frameworks, methodologies, or tools used by others on the team.
• Strong communication and stakeholder-influence skills, with the ability to work effectively with Director-level partners across business and technical teams.
• Ability to navigate ambiguity, make fast data-driven decisions, and operate effectively in a fast-paced, dynamic environment.
• Ability to use AI-native tools to plan, implement, and synthesize analyses across use cases ranging from experiment readouts to retention/churn deep dives.
• BS or MS in Statistics, Mathematics, Operations Research, Computer Science, Engineering, Econometrics, or a related field.

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.