Staff AI Scientist – Business Intelligence

Staff AI Scientist – Business Intelligence About the team Intuit's Business Intelligence (BI) team builds the reporting and analytics experiences that millions of QuickBooks customers and accountants rely on every day, including Modern Reports, Management Reports, KPI Scorecards, Dashboards, and FP&A (Cash Flow, Budgets, Forecasting). The team is in the middle of extending these experiences with AI: conversational reporting through Omni, self-serve report building, and agentic development practices that let us build faster on a codebase spanning years of financial logic. We sit on one of Intuit's largest proprietary datasets, tens of billions of transaction rows across millions of small businesses, and we are scaling our platform (built on StarRocks) to support multi-entity, multi-currency customers at a new order of magnitude. About the role As a Staff AI Scientist on BI, you will be a technical leader spanning multiple BI workstreams, such as Core Reporting, Multi-Entity/Scale, and Conversational Narrative Builder. You will architect AI-native systems and evaluation frameworks that other AI Scientists across BI build on, and you will help set the direction for how BI uses agentic AI to serve accountants and Responsibilities Serve as a technical leader across multiple BI workstreams, influencing product and technology direction beyond a single team. Architect AI-first systems for BI, such as agentic reporting assistants and dimension-aware forecasting engines, including systems where LLMs orchestrate tools and skills across the BI surface area. Build Intuit's AI advantage in BI by deeply leveraging our proprietary financial data (tens of billions of transaction rows across millions of businesses), instituting comprehensive evaluation and monitoring across both traditional and agentic BI systems, and applying novel combinations of ML methods. Lead creation of reusable, versioned data and feature assets from BI's data platform (built on StarRocks) and set data-quality bars for downstream science and product work. Design robust evaluation strategies, including adversarial and edge-case coverage, reflecting the accuracy bar required when BI surfaces present financial figures directly to accountants and business owners. Architect multi-stage agent pipelines spanning the BI science lifecycle, from data preparation through experiment execution to results synthesis, that other AI Scientists across BI can build on. Champion responsible AI practices given the sensitivity of financial data and the accuracy required for customer- and accountant-facing insights. Partner with BI product, design, and engineering leadership to frame ambiguous problems, such as how conversational AI should explain multi-entity consolidated statements, and co-own end-to-end customer solutions. Coach and elevate other AI Scientists and engineers across BI, help them direct and orchestrate agents on real work, and identify tooling or process gaps that block productivity. Qualifications MS or PhD in Computer Science, Statistics, Applied Mathematics, Operations Research, or a related field. 6+ years of applied ML or data science experience, including experience leading technical initiatives that span multiple teams. Deep, hands-on expertise across ML paradigms, including supervised and unsupervised learning, causal inference, time-series methods, and deep learning, along with applied experience designing and evaluating LLM-based and agentic systems. Proven experience architecting end-to-end production ML systems, from data platform integration through deployment and monitoring, ideally at large scale (billions of rows or high query volume). Proficient in Python, SQL, and distributed data systems such as Spark or Hive/SparkSQL, and comfortable partnering directly with data platform and data engineering teams. A track record of mentoring other engineers and scientists and setting technical standards across teams. Excellent communication skills, with the ability to i