Director, Expert Network Planning and Analytics

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, scheduling, and real-time/intraday management — for a multi-segment, multi-geography expert network.
• Drive the growth of Intuit’s Services business by leveraging the expert network at the optimal cost structure.
• Run the operating cadence — planning cycles, business reviews, and peak readiness — with clear risks, dependencies, and outcomes surfaced to VP/SVP stakeholders, against explicit FY27 targets for forecast accuracy, occupancy, cost to serve, and peak service level attainment (aligned to ICS-IG-02 Global Ops KPIs).
• Build and scale the WFM Center of Excellence: consolidate fragmented planning and operations into one platform, one data model, and one set of standards across segments.
• Lead, grow, and develop a global org across the U.S. and India, including an India-based command center; build the bench, raise the analytical and AI fluency of the team, and participate in Intuit’s Data Science community to stay current with the craft and bring its best practices back into WFM.
• Partner cross-functionally with Service Delivery, Finance, Product, Engineering, and HR to align capacity plans to business goals and remove friction between highly technical and operational teams.

Qualifications

Data Science leadership at scale

• 12+ years of progressive experience in data science or advanced analytics teams, including 6+ years managing people managers of data professionals, in a complex, scaled operating environment — this leader should be interchangeable with other Data Science Directors in the craft.
• Demonstrated ownership of enterprise-scale service operations — ideally $1B+ in spend and/or thousands of frontline experts — across multiple geographies and lines of business.
• Deep expertise running operating cadences (planning cycles, business reviews, peak readiness) for a scaled business unit.

Methodology, causal inference, and modeling craft

• Direct, hands-on experience with hierarchical and intermittent-demand forecasting, queuing and Erlang capacity models, constrained optimization for scheduling, and survival modeling for expert attrition.
• Track record of personally directing identification strategy, model specification, and evaluation design — and of isolating causal effect, not just correlation, in operational settings where randomization isn’t possible.
• Track record of moving operations from manual, reactive execution toward model-driven, autonomous decisioning — and of measurably improving forecast accuracy, occupancy, and routing outcomes.

Production rigor and governance

• Experience running models as production systems: committed accuracy thresholds, continuous monitoring, drift detection, versioning, and rollback planning.
• Experience applying Responsible AI practice — fairness and adverse-impact review — to models that allocate work or make decisions affecting a large frontline workforce.
• Fluency with the modeling stack: SQL, Python/R, Databricks, Git, and agentic tooling (MCPs, agents, orchestrators), with the reproducibility and version-control standards to hold a team accountable to it.

AI-native strategy and measurement

• Proven ability to drive strategy, execution, and insight for AI-native operating capabilities across their full lifecycle — ideation, discovery, experimentation, and scaling — rather than treating AI as a bolt-on.
• Rigor in measurement and experiment design: defining the metrics that prove whether a capability is working, and designing the tests that isolate its impact.
• Strong partnership instincts with Product and Engineering to co-create and scale these capabilities, including shaping how humans and models share the decision.

Strategic thinking, systems thinking, and influence

• Strong strategic and systems thinking — the ability to see how forecasting, scheduling, routing, cost, and customer experience interact, and to solve the whole system rather than a local symptom.

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