Lead Product Data Scientist, Forecasting

At Klaviyo, we value the unique backgrounds, experiences and perspectives each Klaviyo (we call ourselves Klaviyos) brings to our workplace each and every day. We believe everyone deserves a fair shot at success and appreciate the experiences each person brings beyond the traditional job requirements. If you’re a close but not exact match with the description, we hope you’ll still consider applying. Want to learn more about life at Klaviyo? Visit klaviyo.com/careers to see how we empower creators to own their own destiny. Lead Product Data Scientist, Forecasting About the Team 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 strategy, financial planning, and long-term growth. As Klaviyo continues to scale its platform, expand its product portfolio, and introduce new AI-powered capabilities, the ability to accurately forecast business outcomes and understand the drivers behind them has never been more important. Our team combines rigorous statistical modeling, advanced analytics, and deep business understanding to help leaders make better decisions. We believe data science should be embedded in the product development lifecycle, influencing strategic investments, identifying opportunities, and enabling teams to move quickly and confidently. About the Role 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. 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. Build scalable root cause analysis frameworks through repeatable methodologies, templates, and dashboards, enabling other data scientists and business stakeholders to investigate performance consistently and efficiently. Develop self-service forecasting and analytics capabilities that empower product managers and cross-functional leaders to explore business trends, evaluate scenarios, and make informed decisions independently. Partner with Data Engineering to build reliable data pipelines and dbt models that provide clean, well-structured, and trustworthy data for forecasting and downstream analysis. Evaluate emerging AI and forecasting technologies , identifying opportunities to leverage new modeling techniques, time series foundation models, and AI-powered tools to improve accuracy, efficiency, and decision support. Influence decisions across the organization by communicating complex statistical findings and business implications clearly and persuasively to stakeholders ranging from individual contributors to executive leadership. Who You Are 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. 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-o