Mid Level Demand Planning Analyst Jobs
Mid level demand planning analyst jobs go to analysts ready to own forecasting cycles end to end, influence cross-functional decisions, and guide junior teammates without close oversight. Roles are spread across on-site, hybrid, and remote settings in Food & Beverage, Manufacturing, and Technology & Software, with Caleres, Wells Enterprises, and Mars actively hiring at this level now.
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Showing 5 of 24+ Mid Level Demand Planning Analyst jobs
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
for forecasting use casesData & 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
- 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
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
for forecasting use casesData & 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
- 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
See All 24 Mid Level Demand Planning Analyst Jobs
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Find JobsMid Level Demand Planning Analyst Job Market
Who's Hiring
- Caleres2
- Wells Enterprises2
- Mars1
- Puig1
- Intuit1
Top Industries Hiring
- Food & Beverage4
- Manufacturing3
- Technology & Software3
- Consumer Goods2
- Fashion & Apparel2
Mid Level Demand Planning Analyst Jobs: Frequently Asked Questions
How do I get a mid level demand planning analyst job?
Position your existing experience around ownership, not just participation. Highlight forecasts you built independently, accuracy improvements you drove, and cross-functional projects where you shaped decisions. Recruiters at this level want evidence you can run a planning cycle with minimal direction, so lead your resume and cover letter with those specific outcomes rather than listing tools or responsibilities.
Which companies hire mid level demand planning analysts?
Companies hiring mid level demand planning analysts right now include Caleres, Wells Enterprises, and Mars, based on current listings on Migrate Mate as of July 2026. Hiring at this level covers large consumer goods companies, retailers, and manufacturers who need analysts capable of running independent forecasting workstreams rather than supporting senior planners.
Are there remote mid level demand planning analyst jobs?
Yes, though availability varies by employer and industry. About 13% of mid level demand planning analyst openings are remote or hybrid as of July 2026, reflecting strong demand for analysts who can collaborate across distributed supply chain and commercial teams. On-site roles are more common at manufacturers and retailers with complex warehouse or store operations.
How do I move up to a mid level demand planning analyst role?
Progression from entry level comes from gradually taking ownership of full planning cycles rather than supporting pieces of them. Build depth in statistical forecasting methods, learn to communicate assumptions to commercial and operations stakeholders, and seek out projects where your accuracy numbers are measurable. Demonstrating that your work influenced an inventory or procurement decision is the clearest signal you are ready for mid level responsibility.
Which industries hire the most mid level demand planning analysts?
Mid Level demand planning analyst roles concentrate in Food & Beverage, Manufacturing, and Technology & Software, based on current listings on Migrate Mate as of July 2026. Those sectors drive hiring at this level because their supply chains involve enough SKU complexity, seasonal variability, or channel diversity to require analysts who can own a category or region independently rather than working from a template.