The opportunity
Klaviyo's Strategic Finance & Analytics team helps leaders understand, measure, and plan for business performance. We work closely with Product, Engineering, Data Science, Go-to-Market, and Finance partners to turn complex data into financial insight that informs investment decisions, planning, and long-term growth.
What you'll do
Build and maintain forecasting models for recognized revenue and COGS. Start: with overall recognized revenue, then extend to consumption-aligned infrastructure costs (message sending and LLM usage first) and, over time, the full COGS base, including cloud infrastructure, SMS and email carrier and vendor costs, AI inference, and support.
Connect product workloads to unit economics. Work with Engineering, Product,: and Finance to link usage drivers (such as message volume, profile growth, channel mix, and AI feature usage) to both revenue and delivery costs.
Build forecasts that scale with the business across multiple time horizons: and levels of granularity, designed to extend to new consumption-based products as they launch.
Partner with Finance and FP&A to understand planning mechanics and modeling: assumptions, and translate them into robust, well-documented frameworks that feed budgeting, rolling forecasts, and long-range planning.
Monitor model performance over time , tracking accuracy and bias, detecting: drift, and building challenger models to keep forecasts resilient.
Lead variance investigations when actuals diverge from forecast, separating: data issues, model limitations, customer behavior, pricing or product changes, and external factors.
What they're looking for
- You have 8+ years of experience in data science, analytics engineering,: strategic finance, or a related quantitative field, with hands-on experience building and deploying forecasting models in production.
- You combine technical depth with finance context. You understand how revenue: recognition, usage, pricing, and cost drivers translate into financial outcomes.
- You have strong foundations in statistics and time series forecasting, and: experience applying methods such as hierarchical, probabilistic, or machine learning approaches to business problems.
- You're highly proficient in SQL and Python or R, with experience building: production-quality analytical solutions, and you're familiar with dbt and modern analytics engineering practices.
- You've worked with cloud data platforms and distributed processing systems.
- You have experience with revenue, pricing, usage-based, or subscription data,: ideally in SaaS or a platform business.
- You're comfortable with imperfect data definitions, evolving metrics, and: tradeoffs between precision and decision usefulness.
- You can turn ambiguous business questions into well-defined analytical: approaches, success metrics, and clear recommendations.
