Manager 3, AI Science (MLOps, AI, Data Science)

Intuit's Data, Growth & Experiences (DGX) organization is building the trusted, AI-ready data foundation that powers Intuit's System of Intelligence — fueling personalized customer experiences, GTM growth, and agent-ready workflows across the company. We're looking for a Manager 3, AI Science (MLOps, AI, Data Science) to lead a multi-team AI Science organization within DGX, spanning three interconnected areas: MarTech (the intelligent, connected capabilities that power customer acquisition, engagement, and retention at scale); Data — across data definition, governance, processing, persistence, query, semantic modeling, and analytics, as we transform Intuit's data ecosystem into a governed, AI-ready knowledge stack anchored by a shared semantic layer; and Data Acquisition (IDX) — the pipelines that connect and standardize data flowing in from third-party systems, making it consumable, agent-actionable, and ready for activation without bespoke integration work.

This role owns technical direction across these capabilities on a 1–3 year horizon, translating DGX's data and AI strategy into production-grade, agent-native architectures. You will lead a team of Data Scientists and AI Scientists building the models, pipelines, and semantic infrastructure that let Intuit's products, GTM teams, and AI agents reason over trusted, governed, real-time data — partnering closely with Product and Engineering leaders to embed AI and agent-driven development into how DGX builds. This is a people-management role with significant technical scope: you're expected to be fairly hands on to shape the science, the data architecture, and platform strategy across all of DGX from an AI science focus and adoption, not just manage delivery.

Responsibilities

Technical & AI/Platform Strategy

• Shape technical direction across DGX capabilities, aligning architecture and roadmaps into cohesive, AI-ready systems that advance DGX's Contextual Knowledge Stack and Business Object Model goals.
• Own the science behind a self-learning semantic layer — infrastructure that encodes business rules and organizational context as reusable knowledge, reconciles fragmented data across various sources in capabilities and products, and verifies its own outputs before they reach a decision-maker or an agent, in the spirit of self-learning cognitive data layers.
• Design verification and eval layers so semantic outputs (entity resolution, metric definitions, business context) are checked before reaching downstream agents or systems, and build feedback loops so definitions improve over time rather than going stale
• Provide leadership in the development of digital-twin models for marketing and sales — audience segments, product lineups, and quote/pricing structures — enabling scenario simulation ahead of product decisions, continuously recalibrated against real transactional, behavioral, and performance data
• Drive anomaly detection as a AI science speciality across the data estate: data quality (missing values, duplicates, distribution shifts), lineage (silent pipeline breaks, drift in 3rd-party IDX feeds), and context/semantic anomalies (inconsistent term usage signaling Business Object Model drift) — surfacing issues before they propagate into GTM decisions or agent actions
• Drive adoption of Intuit's AI/GenAI paved roads and agent skills across integrated systems; embed AI and agent-driven development into products, platforms, and processes, and contribute improvements back to shared platforms
• Guide the team's approach to evaluating and optimizing agentic AI systems — including eval design for non-deterministic, multi-step flows, and judgment on when to shift from prompting to fine-tuning to reinforcement learning

Governance & Product Partnership

• Lead practices for coding, testing, resiliency, security, and compliance across complex, multi-capability systems, including AI/agent guardrails; partner with Directors, Architects, and Program peers on systemic risk
• Partner deeply with Product and Design to understand customer problems and co-create AI-native solutions, including designing tests and shaping how AI surfaces in the product experience
• Partner with Product, Design, and Customer Success to drive experimentation, validate solutions, and scale what delivers lasting impact

Team Leadership & Culture

• Scale leadership and talent systems across teams by empowering managers and tech leads through effective delegation; partner with HR, Product, and Engineering to build a culture of excellence, collaboration, and growth
• Set a high-performance bar with disciplined management; build a strong leadership pipeline and mentor senior ICs and managers to lead AI-native teams effectively
• Build strategies for skills development, mentorship, succession planning, and feedback across the AI Science craft (Algorithmic Modeling, MLOps, Product Sense)

Execution & Operational Discipline

• Lead execution across multiple teams and capabilities, translating strategy into scalable delivery systems
• Align cross-team priorities by weighing business impact, technical health, and innovation; coach leads to strengthen planning
• Drive integrated processes across a complex ecosystem, using data and insights to improve reliability, efficiency, and throughput

Qualifications

Required

• 10+ years of experience in AI/ML, Data Science, or MLOps, including demonstrated technical depth in at least one core area (algorithmic modeling, production ML systems, or applied data science)

• 3+ years of people management experience, including managing principals or senior/staff-level ICs across multiple teams

• Multiple years of experience in applying advanced analytics techniques such as python, ML models, LLMs, etc., is highly preferred.

• Proven experience shaping technical strategy and architecture for AI/ML systems at scale — from data curation through production deployment

• Strong hands-on background in ML Operations: building and operating production-grade models, designing data pipelines, and establishing engineering standards for AI assets

• Experience with semantic layers, knowledge graphs, or entity-resolution systems — building or operating infrastructure that encodes business context and rules as reusable, queryable knowledge rather than static documentation

• Experience building statistical or ML-based anomaly detection for production data systems — data quality, pipeline/lineage monitoring, or drift detection

• Experience partnering cross-functionally with Product, Design, and Engineering to ship AI-driven customer experiences

• Bachelor's degree in Computer Science, Statistics, Data Science, or a related quantitative field; Other applied fields like Cognitive Sciences, Psychology, Economics in combination with advanced degrees in one of the quantitative fields or AI; Master's or PhD preferred

• Strong proficiency in Python and ML frameworks; working knowledge of cloud ML infrastructure (AWS, GCP, Databricks)

Preferred

• Experience with generative AI, LLM-based systems, or agentic AI architectures, including evaluation design for non-deterministic, multi-step systems

• Experience with reinforcement learning, fine-tuning, or prompt optimization techniques and knowing when to apply each

• Experience building simulation or digital-twin models (e.g., audience/segment modeling, pricing/quote simulation, or scenario planning) for marketing, sales, or GTM use cases

• Experience with self-learning or continuously-updating data systems — infrastructure that improves its own definitions, mappings, or verification logic from usage feedback rather than requiring manual upkeep

• Experience with third-party data integration and standardization — normalizing external data (CRM, ERP, ad platforms, etc.) into internal data models at scale

• Track record of building and scaling AI Science or Data Science teams within a large, matrixed technology organization

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