Lead Product Data Scientist, Forecasting

Klaviyo•Boston, MA

About The Position

Klaviyo is seeking a Lead Product Data Scientist, Forecasting, to own and advance the company’s forecasting capabilities. This is a hands-on technical leadership role at the intersection of data science, product strategy, and business performance. The role leads development of revenue forecasting models, establishes evaluation standards, and helps the organization understand how customer behavior, product investments, pricing decisions, and market dynamics affect business outcomes. The role connects advanced statistical methods to business questions and translates findings into recommendations that shape decisions. It partners with Product, Finance, Engineering, and Go-to-Market leadership to build scalable forecasting systems and self-service analytical capabilities. The successful candidate will define forecasting technical direction, mentor data scientists, and establish methodologies and standards that make forecasting a trusted, repeatable practice across the company. Forecasting at Klaviyo informs business planning, product decisions, strategic investments, and understanding of customer and product performance. This role will shape models, methodologies, and analytical standards that influence growth opportunities and organizational decision-making.

Requirements

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.
Deep expertise in time series forecasting, statistical modeling, and model evaluation, including experience with methods such as hierarchical forecasting, probabilistic or Bayesian approaches, and machine learning.
High proficiency in SQL and Python or R, with experience developing production-quality analytical solutions and familiarity with dbt and modern analytics engineering practices.
Demonstrated technical leadership, including setting direction on complex projects, mentoring data scientists, reviewing modeling approaches, and influencing technical standards.
Statistical rigor, business intuition, and understanding of how product development, customer behavior, pricing, and strategic investments translate into measurable outcomes.
Ability to own complex analytical problems from exploration through production deployment, monitoring, and continuous improvement.
Ability to translate ambiguous business questions into analytical approaches, define success metrics, and deliver actionable recommendations.
Strong communication skills, including the ability to explain statistical concepts and modeling tradeoffs to technical and non-technical audiences and senior executives.
Ability to work cross-functionally with Product, Finance, Engineering, and business leaders to influence strategic decisions and improve customer outcomes.
Comfort operating amid ambiguity, moving with urgency, and balancing analytical rigor with speed and iteration in a high-growth product environment.
AI-first mindset, with curiosity and familiarity with emerging technologies such as time series foundation models, LLMs, and agentic tools.
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.

Nice To Haves

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 for 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.

Responsibilities

Own revenue forecasting end to end, from problem framing and data preparation through model development, evaluation, deployment, and ongoing monitoring.
Build forecasting models across multiple time horizons and levels of granularity using hierarchical forecasting, probabilistic and Bayesian methods, classical time series techniques, machine learning, and emerging time series foundation models.
Establish forecasting standards and governance for measuring accuracy, bias, calibration, and uncertainty.
Lead forecasting technical direction, evaluate modeling approaches, review technical designs, mentor data scientists, and improve statistical methodology, code quality, and analytical rigor.
Partner with Product, Finance, Engineering, and Go-to-Market teams on scenario planning, strategic investment evaluation, and translating ambiguous business questions into analytical plans.
Use advanced statistical research, including mixed effects models, clustering, and time series analysis, to identify drivers of customer and product performance and inform product strategy.
Lead root cause investigations when actual business performance diverges from forecasts, identifying potential data, model, customer, product, pricing, or external causes.
Build repeatable root cause analysis methodologies, templates, and dashboards so data scientists and business stakeholders can investigate performance consistently and efficiently.
Develop self-service forecasting and analytics capabilities for product managers and cross-functional leaders.
Partner with Data Engineering to build reliable data pipelines and dbt models for clean, well-structured, trustworthy data.
Evaluate emerging AI and forecasting technologies, including new modeling techniques, time series foundation models, and AI-powered tools.
Communicate statistical findings and business implications clearly and persuasively to stakeholders, including executive leadership.