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About This Role
Overview
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Demand planning is the starting point for how the Global Business Solutions Group (GBSG) understands and prepares for what's coming. An accurate, well\-reasoned demand forecast is the single most important input to how the business plans and operates — it shapes downstream capacity, service levels, and expense, and it gives leaders a trustworthy view of where demand is heading and why. When the forecast is right, everything downstream gets easier; when it's off, the cost shows up quickly in customer experience and margin.
We are looking for a Senior Data Scientist to own demand forecasting for a subset of our GBSG demand planning portfolio. This is an individual contributor role with significant scope and visibility — you will be a primary modeler, analyst, and thought partner on how we forecast demand, quantify its drivers, and continuously improve forecast accuracy across pre\-season, in\-season, and off\-season horizons. You will be expected to break down ambiguous business problems into sound, hypothesis\-driven analysis and translate the results into clear, compelling recommendations that influence cross\-functional leaders.
Responsibilities
### Demand Forecasting Ownership
- Own end\-to\-end demand forecasting across the GBSG demand planning portfolio, from early\-season outlook through real\-time in\-season re\-forecasting.
- Build and maintain models that translate business and customer signals — funnel and trade\-up dynamics, offer acceptance, seasonality, product and usage trends — into accurate, decision\-ready demand forecasts.
- Produce interval\-level, daily, and weekly demand forecasts at the granularity the business needs, along with the assumptions and drivers behind them, so that partners can plan capacity, service, and expense with confidence.
- Quantify and communicate forecast risk through scenario models and confidence intervals (e.g., upside/downside demand cases for peak\-season planning).
### Model Development \& Innovation
- Translate demand\-planning business problems into predictive/prescriptive modeling problems, selecting the appropriate statistical, ML, or AI technique, and independently owning model build, validation, and review using paved\-path tools.
- Advance forecasting methodology — incorporating time\-series models, regression\-based approaches, causal inference, and ML techniques to improve accuracy and reduce forecast error.
- Design and analyze experiments (A/B/n) to test drivers of demand and validate modeling choices, applying descriptive and inferential statistical methods.
- Explore and incorporate new signal sources: marketing spend curves, product funnel data, historical seasonality and usage trends, and macroeconomic indicators.
- Assess where AI/ML meaningfully improves the demand forecast, offering data\-backed recommendations on where new techniques will move the needle versus where they will not.
### Cross\-Functional Partnership \& Influence
- Act as a trusted "translator" between technical and non\-technical partners — framing model outputs as clear demand narratives and using data storytelling to influence working teams and senior stakeholders.
- Partner with Workforce Management, Capacity Planning, and Finance as the consumers of the demand forecast, ensuring the forecast is understood, trusted, and actionable for their downstream capacity, service, and expense decisions.
- Collaborate with Marketing and Product to understand how offer and product strategy shifts demand, and reflect those dynamics in the forecast.
- Define the metrics and KPIs that measure forecast quality and business impact, and provide thought partnership on business cases and learning plans across the portfolio.
### Operational Analytics \& In\-Season Support
- Support real\-time in\-season demand analytics — tracking actuals versus forecast, funnel conversion, and emerging demand signals — and re\-forecast as conditions change.
- Build and maintain dashboards and data products that surface demand trends and forecast risk to operational and leadership audiences.
- Lead post\-season retrospectives on forecast accuracy and bias analysis, and turn the learnings into methodology improvements.
### Data \& Infrastructure
- Write and maintain production\-quality SQL and Python (or R) code against Intuit's datalake, with sound data preparation and workflow management (authoring, scheduling, monitoring).
- 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.
Qualifications
### Required
- 3\+ years of experience in data science or quantitative analytics, with a focus on forecasting, demand planning, or time\-series modeling.
- Strong proficiency in Python (pandas, statsmodels, scikit\-learn) and SQL across large\-scale data environments; comfort with data workflow management.
- Hands\-on experience building, validating, and deploying time\-series or demand forecasting models in a production or operational context, and independently reviewing model quality.
- Ability to break down ambiguous business problems into analytical questions and sound hypotheses, and to find evidence to prove or disprove them.
- Demonstrated ability to communicate and influence through data storytelling — translating model outputs into clear recommendations for 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.
### Preferred
- Experience designing and interpreting experiments (A/B/n) and applying causal inference methods (e.g., propensity score, difference\-in\-differences, synthetic control) to answer business questions.
- Familiarity with applying AI/ML or GenAI techniques to forecasting problems, including measuring model performance (accuracy, latency, cost).
- Experience defining metrics/KPIs for an initiative and partnering across multiple lines of business or a broad portfolio rather than a single product.
- Exposure to workforce management, capacity planning, or contact\-center / expert\-network demand as a consumer of demand forecasts.
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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:
San Diego $149,500 \- $202,500
Salary Context
This $149K-$202K range is above the median for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Intuit, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($176K) sits 9% below the category median. Disclosed range: $149K to $202K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Intuit AI Hiring
Intuit has 12 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, Data Scientist, AI Product Manager. Positions span New York, NY, US, Mountain View, CA, US, San Diego, CA, US. Compensation range: $190K - $328K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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