Required
11+ years in data science, quantitative analytics, or a related field, with a strong focus on forecasting.
Track record of owning high-stakes forecasts end to end and setting forecasting methodology or standards.
Advanced time-series and statistical forecasting skills (ARIMA/SARIMA, exponential smoothing, Prophet, state-space models, and ML approaches like gradient boosting).
Deep experience forecasting subscription/recurring-revenue metrics — subscribers, churn, retention, LTV, and revenue.
Strong proficiency in Python (pandas, statsmodels, scikit-learn) and/or R.
Strong SQL and experience working with large, complex datasets.
Strong understanding of finance/FP&A concepts and subscriber-business unit economics.
Excellent ability to communicate results and influence finance and executive audiences.