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# Staff Data Scientist | CK Intelligence

- **Company:** Intuit Inc.
- **Location:** San Diego, CA, United States
- **Experience:** Expert
- **Salary:** $185,500.0 - $251,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Software as a Service, Data Visualization, Python (Programming Language), Machine Learning, Software Product Management, Power BI, Tableau (Software), Sql Optimization, Large Language Models, Looker Analytics
- **Published:** September 27, 2026
- **Apply:** https://dejobs.org/x/x/101AEF4EC14F468C99ADA63EC17978BA/job/

## About the Role

* 8+ years of experience in data science, analytics, business intelligence, or a related field, ideally within consumer technology, financial technology, marketplaces, or SaaS environments.
 * Proven track record of shaping strategy and influencing executive decision making through data.
 * Experience supporting zero to one products, emerging business models, growth initiatives, or other highly ambiguous problem spaces.
 * Experience measuring or evaluating AI, machine learning, or personalization products, including familiarity with evaluation approaches for generative or non-deterministic experiences such as golden datasets, LLM-as-judge, human review, or quality and latency monitoring.
 * Strong expertise in experimentation, performance measurement, customer analytics, and causal or decision-oriented analysis.
 * Demonstrated ability to define success metrics and learning frameworks for initiatives without established benchmarks.
 * Strong business acumen across customer acquisition, engagement, retention, product strategy, and operations.
 * Expertise in data visualization tools such as Tableau, Power BI, Looker, or similar platforms.
 * Advanced SQL proficiency and experience using Python or R for analysis, modeling, and automation.
 * Exceptional storytelling and communication skills, with the ability to craft clear and compelling business narratives from complex or incomplete data.
 * Demonstrated ability to lead cross-functional initiatives and deliver impact in ambiguous, fast-moving environments.
 * Ability to balance analytical rigor with speed, judgment, and pragmatic decision-making.

## Description

As part of this transformation, we are looking for a Staff Data Scientist to help shape the strategy and measurement of Credit Karma's next generation of AI capabilities.

This role is ideal for someone who thrives in zero-to-one environments at the intersection of data, experimentation, AI evaluation, storytelling, and business leadership. You will help teams evaluate nascent AI capabilities, identify the strongest signals of member and business value, and translate early learnings into clear strategic direction.

Role Overview

As a Staff Data Scientist supporting CK Intelligence, you will own the measurement strategy and analytical direction for Credit Karma's AI products across Chat, Agents, and Insights.

You will partner with leaders across Product, Engineering, Design, and Marketing to assess emerging opportunities, shape experimentation strategy, and determine which AI capabilities should be refined, scaled, or deprioritized. Beyond the execution, you will identify, evaluate, and size AI use cases against business value, and own the evaluation that certifies these experiences are good enough to ship.

You will combine rigorous analysis with strong business judgment to bring clarity to ambiguous questions, create compelling performance narratives, and help leadership make confident investment decisions when the model is imperfect.

Responsibilities

Strategy and Measurement for AI-Native Experiences

Identify, evaluate, and size AI use cases against business value, and apply personalization and automation frameworks to shape what gets built. Build and run AI evaluation, including golden datasets and LLM-as-judge calibration, to certify non-deterministic experiences and diagnose where they need to improve.

Zero-to-One Measurement

Develop measurement strategies for AI products where established benchmarks do not yet exist. Define leading indicators, learning milestones, success criteria, and the quality, latency, and reliability bars an experience must clear before it reaches members.

Experimentation and Causal Rigor

Apply causal inference and counterfactual reasoning to isolate what actually moved a metric. Design and run the experiments and holdouts that resolve whether an AI experience is incremental rather than merely adopted, and own predictive models through their lifecycle.

Strategic Thinking and Business Acumen

Frame the right question before any analysis runs, and meaningfully drive AI product strategy. Generate insights and testable hypotheses that alter roadmaps and resource decisions, and define the business metrics and causal levers the business manages to.

Member Lifecycle Intelligence

Build a deeper understanding of how members discover, adopt, and return to AI experiences, and which jobs-to-be-done actually get solved. Distinguish experiences that create genuine member value from those that shift where existing activity happens.

Data Products and Decision Governance

Build and maintain the data products that CK Intelligence, and its agents, depend on. Prototype the data model, pipeline, and surface, then partner with engineering to harden what sticks, and govern the decision systems between data and member experience.

Agentic Analytics and Delegation

Automate the team's analytical bottlenecks into agentic systems stakeholders run independently. Own the agent context so it inherits the rigor and not just the query, and decide with evidence which analytical work may run without a human in the loop.

Executive-Facing Visualization and Storytelling

Develop compelling visualizations, dashboards, and presentations that enable leadership to quickly understand performance, tradeoffs, and emerging opportunities. Bring clarity to complex or incomplete analytical narratives.

Cross-Functional Influence

Serve as a trusted data science partner to senior leaders across Product, Engineering, Design, and Marketing. Facilitate alignment through data and create shared understanding of hypotheses, performance drivers, risks, and opportunities.

Thought Leadership and Enablement

Champion best practices in experimentation, AI evaluation, measurement, and decision science. Mentor other data scientists and analysts and contribute to a culture of rigorous, data-informed innovation.


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