Lead Strategic Finance & Analytics Engineer

Klaviyo•Boston, MA

About The Position

Klaviyo is hiring a Lead Strategic Finance & Analytics Engineer for its Strategic Finance & Analytics team. The role builds and maintains production-quality models that forecast recognized revenue and cost of goods sold (COGS), combining data science, finance, and data engineering. The initial focus is recognized revenue forecasting, followed by consumption-aligned infrastructure costs such as message sending and LLM usage, and eventually the broader COGS base, including cloud infrastructure, SMS and email carrier and vendor costs, AI inference, and support. The position connects product workloads and customer usage to revenue, delivery costs, and unit economics. It develops scalable forecasts across time horizons and levels of granularity to support budgeting, rolling forecasts, long-range planning, pricing, investment decisions, and margin management. The role owns models end to end, establishes forecasting and code-quality standards, monitors model performance, and partners with FP&A, Product, Engineering, Data Science, Go-to-Market, and Analytics Engineering. It reports into Strategic Finance & Analytics.

Requirements

8+ years of experience in data science, analytics engineering, strategic finance, or a related quantitative field.
Hands-on experience building and deploying forecasting models in production.
Understanding of how revenue recognition, usage, pricing, and cost drivers translate into financial outcomes.
Strong foundations in statistics and time series forecasting, with experience applying methods such as hierarchical, probabilistic, or machine learning approaches to business problems.
High proficiency in SQL and Python or R.
Experience building production-quality analytical solutions and familiarity with dbt and modern analytics engineering practices.
Experience with cloud data platforms and distributed processing systems.
Experience with revenue, pricing, usage-based, or subscription data; SaaS or platform business experience is ideal.
Ability to work with imperfect data definitions, evolving metrics, and tradeoffs between precision and decision usefulness.
Ability to turn ambiguous business questions into well-defined analytical approaches, success metrics, and clear recommendations.
Demonstrated technical leadership, including setting direction on complex projects and influencing standards across teams.
Clear communication skills, including the ability to explain modeling decisions and tradeoffs to technical and non-technical audiences, including senior executives.
AI-first mindset and curiosity about how LLMs and agentic tools can improve forecasting and analytical workflows.
A degree in Statistics, Mathematics, Computer Science, Economics, Finance, Engineering, or another quantitative field, or an advanced degree with a quantitative emphasis; equivalent hands-on experience is equally valued.

Nice To Haves

Experience forecasting COGS, cloud or infrastructure costs, or other unit-cost drivers.
Experience with consumption-based or usage-based pricing models.
Experience supporting FP&A, strategic finance, or investor-facing analysis.
Experience building self-service dashboards or forecasting tools used by Finance and Product partners.
Experience establishing forecast evaluation frameworks or governance practices.
Hands-on exposure to time series foundation models, LLMs, or agentic AI for forecasting and decision support.

Responsibilities

Build and maintain forecasting models for recognized revenue and COGS, expanding from revenue to consumption-aligned infrastructure costs and the full COGS base over time.
Connect product workloads and usage drivers—including message volume, profile growth, channel mix, and AI feature usage—to revenue and delivery costs.
Build scalable forecasts across multiple time horizons and levels of granularity, with designs that can extend to new consumption-based products.
Work with Finance and FP&A to understand planning mechanics and assumptions, translating them into documented frameworks for budgeting, rolling forecasts, and long-range planning.
Monitor forecast accuracy and bias, detect model drift, and develop challenger models.
Lead investigations of forecast variances, distinguishing data issues, model limitations, customer behavior, pricing or product changes, and external factors.
Run scenario and sensitivity analyses for pricing changes, product launches, vendor and infrastructure decisions, and cost-efficiency initiatives.
Partner with Analytics Engineering on reliable pipelines and dbt models that provide a trusted foundation for forecasting.
Establish technical standards for forecasting methodology, evaluation, and code quality.
Communicate modeling decisions, results, and recommendations to technical and non-technical audiences, including executive leadership, using visualizations, narratives, and presentations.
Evaluate emerging techniques, including time series foundation models and AI-assisted workflows, where they can improve accuracy or speed.
Own forecasting models end to end, set technical direction on complex projects, and help grow the team’s forecasting capabilities.