Scenario Planning Data Jobs

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6d ago

Sr Data Scientist, Demand Planning

Intuit San Diego, CA

San Diego, CA

6d ago

$150k - $203k/yr Full time Analytics Big Data Business Intelligence

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Job Description

Intuit offers a benefits-forward culture with a cash bonus, equity rewards, and a comprehensive benefits package. This on-site role is based in San Diego, CA.

Location: San Diego, CA (onsite)

Salary: USD 149,500 - 202,500 per year

Overview

Intuit is seeking a Senior Data Scientist to own demand planning for the Full Service offering within the Consumer Group's Expert Network. You will be the primary modeler and analyst for forecasting Full Service Order volume, translating funnel signals into staffing inputs, and improving forecast accuracy across pre-season, in-season, and off-season horizons. This is an individual-contributor role with high visibility and cross-functional influence on workforce decisions.

What you will do

Lead end-to-end demand forecasting for Full Service, from early-season outlook to real-time in-season adjustments
Develop and maintain models that convert funnel signals (trade-up rates, FSO attach, offer acceptance) into actionable demand inputs for capacity planning
Produce interval-level, daily, and weekly forecasts that inform staffing and partner capacity across internal systems and external channels
Advance forecasting methodology by blending time-series models, regression approaches, and machine learning techniques to improve accuracy
Build scenario models with confidence intervals to quantify risk, including upside and downside demand cases for peak season
Explore and incorporate new signal sources such as marketing spend curves, product funnel data, historical tax filing trends, and macro indicators
Serve as the embedded data science partner for Workforce Management and Capacity Planning, translating outputs into staffing recommendations and operational levers
Collaborate with Finance on demand-to-revenue reconciliation and capacity cost modeling
Work with Marketing and Product on offer strategy and its downstream demand impact, including FSO trade-up promotions and LT offer windows
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
Write and maintain production-quality SQL and Python code against Intuit's datalake datasets related to capacity planning and FSO funnels
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

Requirements

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 in large-scale data environments
Hands-on experience building and deploying time-series or demand forecasting models in production or operational contexts
Ability to work cross-functionally and communicate model outputs to non-technical stakeholders, including senior leaders
Comfort operating in ambiguous, fast-moving environments, especially during high-stakes operational windows
Bachelor's or Master's degree in Statistics, Data Science, Operations Research, Mathematics, or related quantitative field

Technologies

Python
SQL
pandas
statsmodels
scikit-learn
datalake

Benefits

Cash bonus
Equity rewards
Benefits

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