Klaviyo Lead Product Data Scientist, Forecasting at Klaviyo leads development of revenue forecasting models and advances forecasting capabilities. The role combines technical expertise with business judgment to influence product and strategic decisions.
Responsibilities
We're looking for a Lead Product Data Scientist, Forecasting to own and advance Klaviyo's forecasting capabilities, combining deep technical expertise with strong business judgment to influence some of our most important product and strategic decisions.
This is a highly visible, hands-on technical leadership role at the intersection of data science, product strategy, and business performance. You'll lead the development of sophisticated revenue forecasting models, establish rigorous evaluation standards, and help the organization better understand how customer behavior, product investments, pricing decisions, and market dynamics influence business outcomes.
This isn't just about building accurate models or reporting on historical trends. We're looking for someone who can connect advanced statistical methods to real business questions, challenge assumptions, investigate unexpected outcomes, and translate complex findings into clear recommendations that shape decisions.
You'll work closely with Product, Finance, Engineering, and Go-to-Market leadership to build scalable forecasting systems and self-service analytical capabilities. You'll also help define the technical direction of forecasting at Klaviyo, mentor other data scientists, and establish the methodologies and standards that make forecasting a trusted, repeatable practice across the company.
How You'll Make a Difference
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.
Qualification
You bring an AI-first mindsetYou have a degree in StatisticsExperience with revenue forecastingFamiliarity with cloud data platformsWhy This Role Matters
Required
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.
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.
Lead root cause investigations when actual business performance diverges from forecasts, identifying whether changes stem from data quality, model limitations, customer behavior, product or pricing decisions, or external factors.
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.
You thrive in cross-functional environments and enjoy partnering with Product, Finance, Engineering, and business leaders to influence strategic decisions and improve customer outcomes.
You're comfortable operating in ambiguity, move with urgency, and know how to balance analytical rigor with the speed and iteration required in a high-growth product environment.
You bring an AI-first mindset, with curiosity and familiarity around emerging technologies such as time series foundation models, LLMs, and agentic tools that can improve forecasting, analytical workflows, and decision support.
You have a degree in Statistics, Mathematics, Computer Science, Economics, Finance, Engineering, or another quantitative discipline, or an MBA with a quantitative emphasis. Equivalent hands-on experience in applied forecasting or statistical modeling is equally valued.
Preferred
Experience building and scaling forecasting capabilities in a high-growth B2B SaaS or product-focused technology company.
Experience with revenue forecasting, financial modeling, scenario planning, or business performance analysis in a subscription-based business.
Familiarity with cloud data platforms, distributed processing systems, and modern data engineering infrastructure.
Experience applying advanced statistical methods, including mixed effects models, clustering, and probabilistic modeling, to understand customer behavior and product performance.
Experience building self-service analytics tools, dashboards, or forecasting platforms used by cross-functional business stakeholders.
Hands-on exposure to emerging time series foundation models, LLMs, or agentic AI applications for forecasting and decision support.
Experience establishing forecasting governance, model evaluation frameworks, or standardized root cause analysis practices across an organization.
