Senior Data Scientist

About the Role

We're looking for a Senior Forecasting Data Scientist to own and lead the models that drive financial planning for our subscription business. You'll forecast the metrics that matter most subscriber growth, churn and retention, revenue, LTV, and cash flow and turn them into forecasts that Finance, FP&A, and leadership rely on to plan and make decisions.

This senior role sits at the intersection of data science and finance. You'll own the most important forecasts end to end, set the standards and methodology the team uses, and partner closely with FP&A, Growth, and Product to make forecasts accurate, explainable, and actionable. You'll also mentor other analysts and raise the analytical bar.

What You'll Do

• Own and lead forecasting for the subscriber business: new subscribers, churn/retention, reactivation, active base, and net adds.
• Forecast and own financial metrics subscription revenue, ARPU/ARPU trends, LTV, CAC payback, and cash flow and reconcile them with FP&A plans.
• Design cohort and survival models to understand retention dynamics and lifetime value, and set them as team standards.
• Establish rigorous uncertainty quantification confidence intervals and scenario/sensitivity analysis (base, upside, downside).
• Lead the data science contribution to the budgeting, forecasting, and long-range planning cycles alongside FP&A.
• Identify and quantify drivers of subscriber and revenue trends (seasonality, pricing, promotions, marketing spend, macro factors).
• Build reproducible, production-grade forecasting pipelines and clear reporting/dashboards for finance stakeholders.
• Set forecast-accuracy standards (bias, MAPE, backtesting) and continuously improve models.
• Translate complex analysis into clear narratives and recommendations for executives, and influence planning decisions.
• Mentor analysts/data scientists, review methodology, and raise the analytical bar across the team.

What You'll Bring

Required

• 7+ 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.

Nice to Have

• Experience in subscription, SaaS, media/streaming, DTC, or consumer businesses.
• Cohort analysis and survival/churn modeling (e.g., Cox, Kaplan-Meier, BG/NBD).
• Bayesian methods and probabilistic/hierarchical forecasting.
• Data warehouses (Snowflake, BigQuery, Databricks) and BI tools (Tableau, Looker, Power BI).
• dbt, version control (git), and reproducible analytics workflows.
• Driver-based/scenario planning and financial modeling.
• Experience mentoring analysts or leading an analytics workstream.