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 Connected Accounts product team, responsible for using data to improve the member experience around linking, managing, and getting value from connected financial accounts. 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 the value members get from connecting their accounts. Responsibilities • Drive analytics strategy for Connected Accounts: Own the analytics agenda for the Connected Accounts product area, mapping the member journey end to end, from account linking and verification through ongoing engagement and value delivery, 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 connected accounts-specific problems such as link conversion, drop-off, and engagement with account-based features, 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 Connected Accounts to build segmentation that improves targeting, personalization, and feature relevance. • Contribute to the AI-native roadmap: Partner with the team to identify opportunities to apply AI/ML within the connected accounts 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. • 6+ years of experience in data science and analytics, with a track record of driving impact within a product area or initiative; experience with account aggregation, fintech data connectivity, or personal financial management 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. The expected base pay range for this position is: Oakland $194,000 - $262,500

Requirements

Must-haves

Partner with product leadership: Combine data insights with business acumen to partner with Product, Engineering, and Design leaders in shaping roadmap and prioritization decisions.
Build member segmentation: Analyze member behavior within Connected Accounts to build segmentation that improves targeting, personalization, and feature relevance.
Mentor and support the team: Mentor other data scientists on best practices and experimentation rigor, and contribute to hiring and calibration as needed.
We're looking for a curious, proactive data scientist with a passion for driving member engagement and retention.
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.
Oakland $194,000 - $262,500

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlaimachine learning

Platform: career-feeds
Effort: medium