Lead Product Data Scientist, Forecasting

Klaviyo • Boston, MA

4d • $172,000 - $258,000

About The Position

Klaviyo is seeking a Lead Product Data Scientist to own and advance the company’s forecasting capabilities. This highly visible, hands-on technical leadership role sits at the intersection of data science, product strategy, and business performance. The role leads development of revenue forecasting models, establishes rigorous evaluation standards, and helps explain how customer behavior, product investments, pricing decisions, and market dynamics affect business outcomes.

The position owns forecasting end to end, from problem framing and data preparation through model development, evaluation, deployment, and monitoring. It develops forecasts across multiple time horizons and levels of detail, establishes standards for accuracy, bias, calibration, and uncertainty, and leads technical direction and methodology. The role also connects analytical findings to business strategy through scenario planning, root-cause investigations, self-service analytics, and clear recommendations for stakeholders.

The Lead Product Data Scientist partners closely with Product, Finance, Engineering, and Go-to-Market teams, including Data Engineering on reliable data pipelines and dbt models. The role mentors data scientists, creates repeatable forecasting and root-cause analysis practices, evaluates emerging AI and forecasting technologies, and communicates findings to audiences ranging from individual contributors to executive leadership. Klaviyo describes forecasting as a core organizational capability that informs product and business decisions, growth opportunities, investment evaluation, and responses to changing conditions.

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 hierarchical forecasting, probabilistic or Bayesian methods, and machine learning for complex business problems.
• High proficiency in SQL and Python or R, with experience developing production-quality analytical solutions.
• 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 across teams.
• Strong statistical rigor and business intuition, with an understanding of how product development, customer behavior, pricing, and strategic investments translate into measurable business outcomes.
• Ability to own complex analytical problems from exploration through production deployment, ongoing monitoring, and continuous improvement.
• Ability to turn ambiguous business questions into well-defined analytical approaches, establish success metrics, and deliver actionable recommendations.
• Strong communication skills, including explaining statistical concepts and modeling tradeoffs to technical and non-technical audiences, including senior executives.
• Ability to collaborate with Product, Finance, Engineering, and business leaders to influence strategic decisions and improve customer outcomes.
• Comfort operating in ambiguity, moving with urgency, and balancing analytical rigor with speed and iteration in a high-growth product environment.
• AI-first mindset and curiosity and familiarity with emerging technologies such as time-series foundation models, LLMs, and agentic tools that can improve forecasting, analytical workflows, and decision support.
• 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, including problem framing, data preparation, model development, evaluation, deployment, and ongoing monitoring.
• Build forecasting models across multiple time horizons and levels of granularity using appropriate statistical, machine-learning, probabilistic, Bayesian, hierarchical, classical time-series, and emerging foundation-model approaches.
• Define forecasting standards and governance for accuracy, bias, calibration, uncertainty, and model evaluation.
• Lead forecasting technical direction, review technical designs and modeling approaches, 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 analytical plans for ambiguous business questions.
• Use advanced statistical research, including mixed-effects models, clustering, and time-series analysis, to identify customer and product performance drivers.
• Lead investigations into discrepancies between actual business performance and forecasts, identifying possible data, model, customer, product, pricing, or external causes.
• Develop repeatable root-cause analysis methodologies, templates, and dashboards for data scientists and business stakeholders.
• Build self-service forecasting and analytics capabilities for product managers and cross-functional leaders.
• Partner with Data Engineering to develop reliable data pipelines and dbt models for forecasting and downstream analysis.
• 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 to stakeholders, including executive leadership.

Benefits

• The total compensation package may include participation in the company’s annual cash bonus plan.
• The total compensation package may include variable compensation (OTE) for sales and customer success roles.
• Equity may be included in the total compensation package.
• Sign-on payments may be included in the total compensation package.
• A comprehensive range of health, welfare, and wellbeing benefits based on eligibility.
• The company describes its benefits as generous.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior