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About This Role
Overview
Company Overview
Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe everyone should have the opportunity to prosper. We never stop innovating to make that possible.
Team Overview
CK Intelligence is the team building Credit Karma’s AI products. We are developing AI-native experiences across three pillars: Chat, where members ask anything about their finances; Agents, which complete multi-step work on a member’s behalf; and Insights, which proactively surface what changed and what to do next.
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.
Qualifications
- 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.
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:
San Diego $185,500 - $251,000
Salary Context
This $185K-$251K range is above the 75th percentile for Data Scientist roles in our dataset (median: $167K across 172 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Intuit, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($218K) sits 15% above the category median. Disclosed range: $185K to $251K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Intuit AI Hiring
Intuit has 32 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Software Engineer, MLOps Engineer. Positions span Mountain View, CA, US, San Diego, CA, US, New York, NY. Compensation range: $116K - $369K.
Location Context
AI roles in San Francisco pay a median of $264,500 across 2,413 tracked positions. That's 24% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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