Director, Expert Network Planning and Analytics

Overview Intuit is the global financial technology platform that powers prosperity for over 100 million consumers and businesses across TurboTax, Credit Karma, QuickBooks, and Mailchimp. Behind that promise sits Intuit Customer Success (ICS) — the organization that connects tens of thousands of experts to customers at the moments that matter most. We are looking for a Director, Data Science – Expert Network Planning to own end-to-end planning and workforce operations for ICS as a Data Science leader held to the same craft bar as the rest of Intuit’s Data Science community. This leader turns ~$1.5B in annual service spend and a network of 40,000+ experts into the right capacity, in the right place, at the right time — across both our Small Business (GBSG) and Consumer segments, and across peak events that can double service demand in a single day. This is a Data Science role, not a traditional staffing role. We are rebuilding Workforce Management as an AI-native, autonomous operating capability, and this leader owns the methodology behind it — the forecasting, causal inference, and optimization models that increasingly make planning, scheduling, and intraday decisions with defined, governed autonomy. You will lead a global organization of 45+ FTE and 50+ CWs across the U.S. and India — including data scientists, applied scientists, and business analysts, alongside planners and command-center staff whose work will itself change as the team leans further into modeling, technology, and automation — and partner across Product, Engineering, Finance, and Service Delivery to build a Workforce Management Center of Excellence for the enterprise, while remaining an active member of Intuit’s broader Data Science community. Responsibilities Methodology and craft ownership Method ownership: personally review and sign off on identification strategy, model specification, and evaluation design for any model above a defined impact threshold — direct the methodology rather than simply handing requirements to a separate builder function. Causal inference: isolate the causal effect of scheduling, routing, and staffing changes on service level, cost, and business impact in settings where randomization or controlled experiments aren’t possible; own this as a named accountability, not an implied one. Named methods: set the forecasting and capacity strategy end to end using hierarchical and intermittent-demand forecasting, queuing and Erlang capacity models, constrained optimization for scheduling, and survival modeling for expert attrition — alongside supply modeling, occupancy and shrinkage optimization, and routing decisions that balance customer experience against cost. Own the WFM P&L lens at ~$1.5B scale: quantify trade-offs between service level, cost, and expert experience, and translate them into decisions leadership can act on. Autonomous operations and governance Delegation governance (headline responsibility): decide which planning and staffing decisions the team’s platform makes on its own versus which require human approval — back that line with measured error rates and the cost of being wrong, and widen it as the models earn it. Metric ownership: own certified definitions — not just targets — for forecast accuracy, occupancy, shrinkage, and cost to serve, enforced consistently across Finance, Service Delivery, Data Science, AI Science, and Product. Production rigor: run models as production systems — published assets with committed accuracy thresholds, continuous monitoring, drift detection, model versioning, and a defined rollback plan for when a peak event breaks the assumptions a model was fit on. Model risk and RAI: own fairness and adverse-impact review of scheduling and routing allocation across 40,000+ experts, and adherence to Intuit’s Responsible AI review and process. Business and organizational leadership Own the full planning stack — long-range capacity, annual and quarterly operating plans, peak/seasonal planning, schedulin…

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