Lead Data Scientist, Forecasting

About the role

To thrive as a company and meet our impact goals, we must cultivate a culture of high-performance. You can read about our culture principles here.

We know managers are often the single-largest driver of employee satisfaction and growth, and our talent is our biggest asset. Because of that, we’ve identified consistent expectations for all of Guild’s people managers — helping you know what to expect from your experience here.

If you are an Internal Candidate, please apply via our Internal Job Board.

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Guild is hiring a Lead Data Scientist, Forecasting to scale the impact of our Forecasting and Financial Analytics team. You'll own and improve critical components of our most complex and most important forecasting model — working inside a modern, modular forecasting system and experimentation framework to drive step-change improvements in how well we forecast clients' education benefit program.

As a Lead Data Scientist, Forecasting, you will:

- Develop and improve the usage-based forecasting models for critical components of clients' education benefit with Guild, using advanced statistical and machine learning techniques.

- Raise forecast accuracy and shorten the monthly refresh cycle, using a backtesting and experimentation framework to test methodological changes with rigor. Build tooling and process to catch correctable model issues.

- Transfer modeling logic out of individual heads into well-structured, documented, tested code, building redundancy on a business-critical forecast.

- Partner cross-functionally with Analytics, Data Science, Data Engineering, Product, and Technology teams to improve the data, systems, and infrastructure the forecast depends on.

- Mentor and guide junior team members, fostering a culture of continuous learning and innovation.

- Produce insights, analyses, and recommendations to help internal stakeholders understand forecast drivers and the impact on client and internal business metrics.

You are a strong fit for this role if you have:

- 5+ years of total experience in analytics, data science, forecasting, or financial planning & analysis.

- Strong experience with SQL and experience transforming and structuring large datasets into reusable formats.

- Proficiency in Python and/or R, with depth across time-series and machine learning forecasting methods and sound judgment about the tradeoffs among accuracy, interpretability, and stability.

- Experience operationalizing models — backtesting, validation, monitoring, drift detection, retraining — ideally on a platform such as Databricks with experiment tracking (e.g., MLflow).

- A track record of getting productive quickly in an existing codebase and improving it collaboratively.

- Experience with Looker, Tableau, Omni, or similar data visualization tool.

- Fluency with AI-assisted development tools such as Cursor, Claude Code, or similar coding agents.