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About This Role
Overview
Join the Intuit Customer Success Data Science & Analytics team as the Manager 3 leading Data Foundations. This team builds the governed, compliant data foundation that Intuit's Customer Success analytics and AI capabilities are built on. Metric definitions, clean domain entities, customer journey data products, semantic knowledge layer and the agentic tooling that keeps them healthy. You will lead a team of senior and staff-level data scientists accountable for building the Governed Data Foundation for ICS. This is a hands-on-adjacent people-leadership role. You will set technical direction, own the roadmap and its financial case, grow domain owners, and influence at the Director and VP level. The work spans two halves that must hold together: building data capabilities (consuming models, automating pipeline development and root-cause analysis, generating insights) and building the knowledge layer (understanding the data deeply enough that governed definitions and semantics can be trusted by downstream agents).This role is for someone who thrives in ambiguity, sets standards that outlast individual projects, and builds both systems and people that scale across teams and business units. Responsibilities
Team Leadership & Talent
- Lead, coach, and grow a team of senior, staff, and principal data scientists; develop domain leads capable of owning a data domain end to end, from source instrumentation through consumption.
- 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
Salary Context
This $234K-$316K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Intuit, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($275K) sits 30% above the category median. Disclosed range: $234K to $316K.
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
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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