Sr Data Scientist, Demand Planning

Responsibilities

• Demand Forecasting Ownership
• Own end-to-end demand forecasting for Full Service, from early-season outlook through real-time in-season adjustments
• Build and maintain models that translate customer funnel signals (trade-up rates, FSO attach, offer acceptance) into workable demand inputs for capacity planning
• Develop interval-level, daily, and weekly forecasts that feed directly into staffing and partner capacity decisions across internal, JDA, CNX, and other TPA channels
• Model Development & Innovation
• Advance forecasting methodology — incorporating time-series models, regression-based approaches, and ML techniques to improve accuracy and reduce forecast error
• Build scenario models and confidence intervals to support risk quantification (e.g., upside/downside demand cases for peak season planning)
• Explore and incorporate new signal sources: marketing spend curves, product funnel data, historical tax filing trends, macroeconomic indicators
• Cross-Functional Partnership
• Serve as the embedded DS partner for Workforce Management and Capacity Planning, translating model outputs into staffing recommendations and operational levers
• Partner with Finance on demand-to-revenue reconciliation and capacity cost modeling
• Collaborate with Marketing and Product on offer strategy and its downstream demand impact (e.g., FSO trade-up promotions, LT offer windows)
• Operational Analytics & In-Season Support
• Support real-time in-season analytics — tracking WIP burndown, FSO funnel conversion, and coverage gap signals
• Build and maintain dashboards and data products that surface demand risk to operational and leadership audiences
• Contribute to post-season retrospectives on forecast accuracy, bias analysis, and methodology improvements

Qualifications

• 3+ years of experience in data science or quantitative analytics, with a focus on forecasting, demand planning, or supply-demand modeling
• Strong proficiency in Python (pandas, statsmodels, scikit-learn) and SQL across large-scale data environments
• Hands-on experience building and deploying time-series or demand forecasting models in a production or operational context
• Demonstrated ability to work cross-functionally and communicate model outputs to non-technical stakeholders, including senior leaders
• Comfort operating in ambiguous, fast-moving environments — particularly during high-stakes operational windows
• Bachelor's or Master's degree in Statistics, Data Science, Operations Research, Mathematics, or a related quantitative field

Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.