← back to jobs
> job detail
C
🧪Data Scientist

Senior Data Scientist, GBSG Demand Planning

Credit Karma · San Diego, California
// classified as
Data Scientist (Modeling, experiments, research.)
posted
1d ago
location
San Diego, California
languages
python, r, sql
tools
—
> stack
pythonrsqlpandas
> description

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.


Footer

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
Apply at Credit Karma →