Own hiring, onboarding, performance management, calibration, and promotion advocacy for the team; build and maintain a strong talent pipeline and participate actively in A4A and talent assessment forums.
Support craft progression across the team and raise the technical bar through review standards, documented best practices, and internal forums.
Scale yourself through delegation and clear ownership boundaries; role-model an inclusive, high-trust environment that encourages constructive debate.
Strategy & Business Outcomes
Own the strategic vision, roadmap, success criteria, and financial case for the Data Foundations portfolio, partner with Finance and TPM to quantify and defend impact.
Develop KPI frameworks that set direction across the organization, applying a 'define once, calculate uniformly' metric standardization model across core data domains.
Apply first-principles thinking to translate Customer Success business strategy into analytical and data-architecture problems at the Business Unit level.
Combine insights, business acumen, and industry benchmarks to influence cross-functional leaders, represent the team's work in executive forums and organization-wide reviews.
Data Foundations & Governance
Drive a shift-left approach to data quality by embedding data design into the product development lifecycle so defects are prevented at source rather than cleaned downstream.
Establish governed clean entities, standardized metric layers, and semantic/knowledge layers as the trusted source of truth for Customer Success data domains.
Own end-to-end customer journey data products, stitching behavioral clickstream, contact touchpoints, and time-series journeys to pinpoint drop-offs and high-contact moments that drive cost-to-serve.
Lead compliance and security execution for the organization's data estate by field-level access control, data-access management, and PII minimization against enterprise mandates and hard regulatory deadlines.
Negotiate and maintain clear ownership boundaries with Data Engineering and PD partners on governed base tables, paved-path platforms, and the graduation of analyst-built assets into managed ownership.
AI Capabilities & Automation
Lead custom implementations of AI/GenAI capabilities for key business initiatives, in partnership with AI and platform teams.
Direct the build-out of agentic data tooling for improving productivity and automation like pipeline migration and development agents, automated data-quality detection, and automated root-cause analysis targeting material reductions in downstream failures, detection time, and analyst hours spent on data bug management.
Guide the team on measurement of AI-native experiences, connecting model performance metrics (cost, latency, accuracy, hallucination) to customer and business outcomes.
Be a thought partner on build/buy decisions for analytics workflow tooling, and identify where new methodologies should be adopted and standardized across the organization.
Qualifications
Required
BS or MS in Statistics, Mathematics, Computer Science, Economics/Econometrics, Operations Research, Engineering, or a related quantitative field
14+ years of experience in data science, analytics, or data platform work, including 5+ years directly managing data scientists or analytics engineers.
Demonstrated success leading teams that deliver governed data foundations at enterprise scale metric standardization, domain/entity modeling, data quality programs, or data product ownership.
Strong hands-on technical foundation in SQL and Python sufficient to set architectural direction, review your team's work critically, and remain credible in deep technical discussions. This role requires a leader who has coded and can still read code, not one who has moved fully into administration.
Experience with modern cloud data platforms and pipeline orchestration, and with migrating legacy analytics estates onto governed, paved-path platforms.
Proven track record building AI/GenAI or agentic capabilities that automate data operations like validation, quality monitoring, root-cause analysis, or insight generation.
Demonstrated ability to define metrics that shape business strategy and to influence Director- and VP-level stakeholders without direct authority.
Experience owning a multi-million-dollar impact portfolio, including building the financial case and reporting against it in executive forums.
Excellent communication skills, with the ability to translate complex technical and data-governance concepts into clear narratives that drive executive decisions.
Preferred
Experience with data governance and compliance regimes (e.g., IRS 7216, GDPR, CCPA) and with implementing fine-grained access control at scale.
Background in semantic layers, knowledge graphs, or knowledge-layer design for AI/agent consumption.
Experimentation and causal inference depth (A/B testing at scale, difference-in-differences, propensity methods, synthetic control).
Experience in Customer Success, customer experience, contact center, or FinTech analytics domains.
Track record of standing up cross-functional governance mechanisms like schema/event registries, data stewardship roles, or change-notification processes for downstream consumers.
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: Mountain View $234,000 - $316,500
