Intuit Manager Demand Forecasting - Data Analytics 

United States, California, San Diego 

923172937

This is not a traditional planning role—You will lead a team that applies advanced modeling techniques, scenario analysis, and deep business context to forecast volume, case mix, seasonality, and other demand drivers that influence how we staff, budget, and operate.

• Own the enterprise-wide demand forecasting strategy , ensuring it is rigorous, data-informed, and embedded in operational and financial planning cycles.
• Design and lead the development of predictive models using time series analysis, machine learning, and simulation methods to forecast future workforce demand.
• Translate complex business signals into model features , accounting for drivers such as customer volume, operational complexity, product launches, and policy changes.
• Manage and grow a high-performing team of data scientists and analysts who specialize in demand modeling and scenario planning.
• Operationalize forecasting outputs by delivering timely, accurate, and scenario-based projections to partners across Finance, Capacity Planning, and Operations.
• Build scalable infrastructure to support automated pipelines, forecast versioning, model monitoring, and reproducibility in partnership with data engineering.
• Continuously evaluate and improve forecast performance , incorporating new data sources, evolving business needs, and changes in planning granularity.
• Influence key decisions through clear communication of forecast insights , risks, and trade-offs with executive stakeholders.
• Foster a planning culture grounded in scientific thinking , modeling excellence, and strategic relevance.

Southern California: $222,000 - $300,000

This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at

• 10+ years of experience in data science, forecasting, or quantitative workforce planning, with a proven track record of leading high-impact technical teams.
• Expertise in applied statistics, time series forecasting, machine learning, and experimentation.
• Proficiency in Python or R, SQL, and modern ML/forecasting libraries (e.g., Prophet, scikit-learn, XGBoost, PyTorch, TensorFlow).
• Deep understanding of workforce planning principles—capacity, productivity, SLAs, and staffing models.
• Demonstrated ability to apply data science to complex operational and strategic business problems.
• Strong communication skills with experience influencing cross-functional executives.
• Experience building forecasting systems at scale, including model management and data pipeline integration.