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About This Role
Overview Intuit is seeking an experienced Data Science leader to manage our People Analytics Data Science & Research team at Intuit. Our team partners closely with Intuit's HR leaders, COEs, Finance, and business partners to deliver data-driven insights that shape our people strategies and elevate decision-making across the company. We are looking for an experienced People Analytics Data Science Leader to set analytical direction across workforce forecasting, employee research and listening, talent and mobility analytics, and the AI-native tools that put insight directly in leaders' hands. The portfolio spans predictive modeling, psychometric research, and applied AI, and it continues to evolve. This leader will lead a distributed analytics team and will be accountable for the rigor of the work before it reaches executive audiences.
This is a leadership role for someone who has not stopped being a scientist: you will develop the team and you will personally review model design, challenge assumptions and interpretability, and hold the methodological bar. This role is critical to supporting Intuit's ongoing organizational transformation, ensuring that how we plan, organize, and develop our workforce is scalable, insight-driven, and aligned to our mission of powering prosperity for our people and the company.
Responsibilities Strategic Thinking and Business Acumen
- Set analytical and modeling direction across workforce forecasting and organizational effectiveness, Voice of Employee research, and AI-native tooling, ensuring consistency and rigor across all domains.
- Apply first-principles thinking to turn People & Places strategy into analytical problems at the function level, and propose and lead the initiatives that follow from it.
- Connect insights across hiring, workforce planning, performance, internal mobility, and employee experience to surface patterns and trade-offs only visible at the portfolio level, and translate them into decision-ready leadership recommendations.
- Combine insights, business acumen, and industry-wide learnings to influence VP-level and above stakeholders, and stand behind a recommendation under scrutiny.
- Partner with P&P COEs, the HRBP community, and Finance leadership to resolve workforce-data definitional questions, including how hires and headcount are defined and reconciled, and represent that work credibly with senior stakeholders.
Strategy and Measurement for AI-Native Experiences
- Co-create the analytics strategy for AI-native decision tools in partnership with cross-functional teams, drawing on industry developments, strategic insight, and domain knowledge.
- Help design strategic frameworks and success measurement across key People & Places initiatives, including AI-powered work.
- Guide teams toward the data sets, insights, and methodological approaches that improve model outcomes, bringing in both external and internal sources.
- Influence the analytics product roadmap so the portfolio continues to improve insight and decision-making across the employee lifecycle.
Inference and Algorithms
- Guide the data science behind workforce planning and organizational effectiveness measures, ensuring the models leaders rely on are sound and consistently applied.
- Review and challenge the team's models with accountability for assumptions, bias, and interpretability, guiding teams from ambiguous business questions to end-to-end analytical solutions.
- Build, review, and scale advanced predictive and prescriptive models, applying rigor across experimentation and causal inference.
- Identify new methodologies and external trends and adapt them to People & Places use cases, creating shareable frameworks that clarify when and how a new approach should be used.
- Act as a thought partner on build versus buy decisions for analytics workflow tooling, identifying the pain points worth solving.
Leadership
- Lead a distributed team of data scientists and researchers, deepening both technical craft and the influence that turns analysis into decisions.
- Set a clear vision for the team, build a high-performance culture, and drive winning results.
- Own intake and demand management with P&P and business partners, shaping requests into clearly defined deliverables and outcomes.
- Build feedback loops into how the team works, tracking whether analysis changed a decision and using that to steer where the team invests next.
- Establish how the team works: standards for methodology review, documentation, and handoff, so quality does not depend on who happens to pick up the work.
Qualifications
- 10+ years in people analytics, with deep data science and applied research experience in a people or workforce context.
- 4+ years of experience managing analytics or data science teams; experience managing a multi-team or multi-domain organization strongly preferred.
- Demonstrated depth in predictive and prescriptive modeling, experimentation, and causal inference, sufficient to review and improve others' methodological choices rather than only interpret their conclusions.
- Fluency in SQL and Python or R, with comfort in modern cloud data environments and data workflow management tooling.
- Experience across workforce domains such as headcount forecasting, attrition and retention modeling, hiring funnel analytics, compensation, or organizational effectiveness, and with survey and psychometric research methods.
- Experience building or measuring AI-native analytics products, with a clear point of view on how to evaluate them and how model performance connects to business outcomes.
- Proven experience establishing shared data definitions and reporting standards across functions with competing sources of truth.
- Demonstrated ability to influence senior executives through storytelling, translating complex analysis into a narrative that changes a decision rather than describing a situation.
- Track record of building feedback loops into analytics work, so the team knows what changed as a result and invests accordingly.
- Passion for enabling data-driven, equitable people processes that foster growth and inclusion.
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
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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Intuit AI Hiring
Intuit has 26 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Data Scientist, AI Software Engineer. Positions span San Diego, CA, US, San Diego, CA, Mountain View, CA, US. Compensation range: $190K - $335K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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