Sr Data Scientist, Demand Planning

Sr Data Scientist, Demand Planning Category Data Location San Diego, California Job ID 22478 Apply Now Share Job Save Job Company Overview Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible. Job Overview TurboTax Live Full Service is one of Intuit's fastest-growing and most operationally complex offerings — connecting customers with expert tax preparers to complete their returns end-to-end. Getting demand planning right is mission-critical: too little supply means customers wait; too much means stranded expert capacity and margin pressure. We are looking for a Senior Data Scientist to own demand planning for the Full Service business within the Consumer Group's Expert Network. This is an individual contributor role with significant scope and visibility — you will be the primary modeler, analyst, and thought partner on how we forecast FSO (Full Service Order) volume, convert funnel signals into staffing requirements, and improve forecast accuracy across pre-season, in-season, and off-season horizons. 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 Data & Infrastructure Write and maintain production-quality SQL and Python code against Intuit's datalake (e.g., capacityplandatasetv3, FSO funnel tables, expert supply tables) Partner with Data Engineering to improve upstream data quality and pipeline 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 Data & Infrastructure Write and maintain production-quality SQL and Python code against Intuit's datalake (e.g., capacityplandatasetv3, FSO funnel tables, expert supply tables) Partner with Data Engineering to improve upstream data quality and pipeline reliability for forecasting use cases Document models, assumptions, and methodologies to enable reproducibility and stakeholder trust for forecasting use cases Document models, assumptions, and methodologies to enable reproducibility and stakeholder trust Qualifications Required 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 Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may 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 Intuit®: Careers | Benefits). 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. The expected base pay range for this position is: Southern California Area: $149,500 - $202,500

Salary : $149,500 - $202,500