Intuit
Director, Expert Network Planning and Analytics
$278K–$376K
San Diego, California · Director · Full-time · Posted Aug 17, 2026
- Experience
- 12+ years in data science or advanced analytics, including 6+ years managing people managers of data professionals.
- Education
- Bachelor’s degree in a quantitative, analytical, or technical discipline required; advanced degree (PhD, quantitative Master’s, or MBA) preferred.
About the job
Leads data science and workforce planning for Intuit Customer Success, building AI-driven forecasting, scheduling, and operational decision systems for a global network of experts.
What you'll do
- Own forecasting, causal inference, optimization, and model governance for capacity planning, scheduling, routing, and intraday operations.
- Lead and develop a global workforce planning organization and establish an enterprise Workforce Management Center of Excellence.
- Set operational metrics and planning cadence, and guide service, cost, and expert-experience decisions with senior leaders.
What they ask for
- 12+ years of progressive data science or advanced analytics experience, including 6+ years managing people managers of data professionals.
- Hands-on expertise in hierarchical and intermittent-demand forecasting, queuing and Erlang capacity models, constrained scheduling optimization, and survival modeling.
- Experience directing model design and evaluation, measuring causal effects, and operating models in production with monitoring, drift detection, versioning, and rollback.
- Fluency with SQL, Python/R, Databricks, Git, and agentic tooling; experience applying Responsible AI fairness and adverse-impact reviews.
Nice to have
- Advanced degree (PhD, quantitative Master’s, or MBA).
- Enterprise-scale service operations experience, ideally $1B+ in spend and/or thousands of frontline experts across multiple geographies and business lines.
Pay
San Diego $278,000 - $376,000 USD base salary
Tools
SQL, Python/R, Databricks, Git, MCPs, agents, orchestrators
AI focus
Build an AI-native, increasingly autonomous workforce management capability using forecasting, causal inference, optimization, and governed decision systems; define human approval boundaries, evaluate model impact, and apply Responsible AI practices.
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