Requirements 8+ years of experience in data science or applied statistics roles, with at least 3 years focused on forecasting, time series modeling, or revenue/enrollment prediction in a SaaS, healthcare, or similar recurring-revenue business Deep hands‑on proficiency in Python (e.g., pandas, numpy, scikit‑learn, statsmodels, Prophet or similar libraries) and SQL, with a track record of taking models from discovery through deployment and ongoing monitoring Strong grounding in statistical and machine learning methods for forecasting (e.g. hierarchical or panel forecasting, gradient boosting, generalized linear models), and a practical sense for when simple models outperform complex ones Experience designing and maintaining production data science systems in partnership with data engineering and platform teams, including versioning, backtesting, performance monitoring, and alerting Comfort working with messy, real‑world commercial data (CRM, marketing, product/event, and financial data) and building robust pipelines and features that can support recurring forecast runs Demonstrated ability to translate ambiguous business questions into well‑scoped technical problems, communicate tradeoffs clearly to non‑technical stakeholders, and incorporate feedback into model and metric design Proven experience influencing cross‑functional partners (e.g., Commercial Operations, Sales, Marketing, Finance) using
