Drimva

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.

Model-written summary · the original posting is authoritative

First seen Oct 28, 2026 · Last verified Nov 5, 2026 (UTC)