The opportunity
Klaviyo's Product Data Science team plays a critical role in shaping how we understand, measure, and improve business performance. We partner closely with Product, Engineering, Finance, and Go-to-Market teams to turn complex data into actionable insights that inform product…
What you'll do
Own revenue forecasting end to end , from problem framing and data: preparation through model development, evaluation, deployment, and ongoing monitoring, ensuring forecasts serve as trusted inputs to business planning and product decisions.
Build sophisticated forecasting models across multiple time horizons and: levels of granularity, leveraging hierarchical forecasting, probabilistic and Bayesian methods, classical time series techniques, machine learning, and emerging time series foundation models.
Establish forecasting standards and governance by defining how the: organization measures accuracy, bias, calibration, and uncertainty, creating consistent and rigorous approaches to model evaluation and performance.
Lead the technical direction of forecasting , evaluating modeling approaches,: reviewing technical designs, mentoring other data scientists, and raising the bar for statistical methodology, code quality, and analytical rigor.
Connect forecasting to business strategy by partnering with Product, Finance,: Engineering, and Go-to-Market teams to support scenario planning, evaluate strategic investments, and translate ambiguous business questions into actionable analytical plans.
Uncover drivers of customer and product performance through advanced: statistical research, including mixed effects models, clustering, time series analysis, and other techniques that reveal meaningful patterns and inform product strategy.
What they're looking for
- You have 10+ years of experience in data science, product analytics, applied: statistics, or a related quantitative discipline, including significant hands-on experience building and deploying forecasting solutions in production.
- You bring deep expertise in time series forecasting, statistical modeling,: and model evaluation, with experience applying methods such as hierarchical forecasting, probabilistic or Bayesian approaches, and machine learning to complex business problems.
- You're highly proficient in SQL and Python or R, with experience developing: production-quality analytical solutions and familiarity with dbt and modern analytics engineering practices.
- You've demonstrated technical leadership by setting direction on complex: projects, mentoring other data scientists, reviewing modeling approaches, and influencing technical standards across teams.
- You combine statistical rigor with strong business intuition and understand: how product development, customer behavior, pricing, and strategic investments translate into measurable financial and business outcomes.
- You're comfortable owning complex analytical problems from initial: exploration through production deployment, ongoing monitoring, and continuous improvement.
- You can translate ambiguous business questions into well-defined analytical: approaches, establish meaningful success metrics, and deliver actionable recommendations.
- You're a compelling communicator who can explain sophisticated statistical: concepts and modeling tradeoffs to both technical and non-technical audiences, including senior executives.