Confirmed on the employer's own hiring board on Nov 3, 2026. First seen by Alion on Nov 2, 2026.
Join Guild, a leading education benefits platform, as a Lead Data Scientist specializing in forecasting. In this role, you will own and improve critical components of our complex forecasting model, develop usage-based forecasting models, raise forecast accuracy, and mentor junior team members. You will also partner cross-functionally with various teams to improve the data, systems, and infrastructure the forecast depends on. Enjoy a range of benefits including wellness resources, education assistance, equity stake, and more.
Missions
- Lead and improve usage-based forecasting models for clients' education benefits, utilizing advanced statistical and machine learning techniques.
- Enhance forecast accuracy and reduce the monthly refresh cycle by implementing rigorous backtesting and experimentation frameworks.
- Collaborate cross-functionally with various teams to improve the data, systems, and infrastructure that the forecast relies on.
Profil recherché
- Experience with Looker, Tableau, Omni, or similar data visualization tool- 5+ years of total experience in analytics, data science, forecasting, or financial planning & analysis
- Experience operationalizing models - backtesting, validation, monitoring, drift detection, retraining - ideally on a platform such as Databricks with experiment tracking (e.g., MLflow)
- 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
- The ability to translate forecasts, including their uncertainty, into clear recommendations for non-technical stakeholders
- Fluency with AI-assisted development tools such as Cursor, Claude Code, or similar coding agents
- Strong experience with SQL and experience transforming and structuring large datasets into reusable formats
- A track record of getting productive quickly in an existing codebase and improving it collaboratively

