Klaviyo Lead Product Data Scientist, Agents at Klaviyo to own and advance measurement and data science strategy for AI marketing and customer agents. Define metrics and analytical foundations to evaluate AI agent performance and business impact.
Responsibilities
We're looking for a Lead Product Data Scientist, Agents to own and advance Klaviyo's measurement and data science strategy across Composer and Customer Agent, combining deep technical expertise with strong product and business judgment to influence some of our most important AI investments.
This is a highly visible, hands-on technical leadership role at the intersection of data science, AI product development, and business strategy. You'll define how we measure the adoption, engagement, quality, cost, and business impact of our AI agents, establishing the analytical foundations that help teams understand what creates meaningful customer value.
This isn't just about building dashboards or tracking traditional product metrics. We're looking for someone who can tackle emerging measurement challenges, develop rigorous approaches to evaluating agent performance, and determine whether AI-powered experiences are genuinely improving customer outcomes. You'll combine experimentation, causal inference, and advanced analytics to help leaders make informed decisions in an environment where established measurement frameworks are still evolving.
You'll partner closely with Product, Engineering, Finance, and executive leadership to shape product strategy, evaluate pricing and monetization opportunities, and translate complex analytical findings into actionable recommendations. You'll also help define the technical direction of agent analytics at Klaviyo, mentor other data scientists, and establish the shared metrics, methodologies, and data foundations that enable teams to make confident, data-driven decisions.
How You'll Make a Difference
Own agent measurement end to end across Composer and Customer Agent, developing comprehensive measurement frameworks that connect adoption, engagement, agent performance, customer value, and revenue impact.
Define what successful agent performance looks like by partnering with Product and Engineering to establish meaningful measures of agent effectiveness, task completion, customer outcomes, failure rates, and the trade-offs between quality, cost, and business value.
Build scalable agent evaluation frameworks by combining conversation data, agent traces, tool-call outcomes, human evaluation, and LLM-based classification to assess quality, identify failure patterns, and understand how changes to models and agent behavior influence product performance.
Measure causal impact and incremental value through experimentation and advanced statistical methods, including quasi-experimental approaches such as difference-in-differences, matched comparisons, staged rollouts, and synthetic controls, to determine how AI agents influence customer behavior, retention, and revenue.
Connect agent performance to business strategy by partnering with Finance and Product teams to evaluate usage-based pricing, packaging, credit economics, model costs, conversion, and the financial implications of product investments.
Establish trusted metrics and analytical standards by defining consistent measurement methodologies, governed data models, and evaluation practices that enable teams to make informed decisions as AI products and underlying technologies evolve.
Lead with an AI-first approach to analytics, leveraging coding agents, LLMs, and emerging tools to accelerate SQL development, data modeling, dashboard creation, conversation classification, and analytical workflows while maintaining rigorous validation and quality standards.
Qualification
You're comfortable tackling ambiguousYou have a degree in StatisticsExperience building governed metricsExperience applying classificationFamiliarity with marketing attributionBackground in marketing technology
Required
You have 10+ years of experience in data science, product analytics, applied statistics, or a related quantitative discipline, including recent hands-on experience measuring AI, machine learning, or LLM-powered products in production.
You bring deep expertise in product measurement, experimentation, statistical modeling, and causal inference, with the ability to select and defend rigorous analytical approaches when traditional randomized experiments aren't feasible.
You have experience evaluating AI systems in production, including designing evaluation frameworks, analyzing agent interactions or conversations, leveraging human labeling or LLM-as-judge methodologies, and understanding how model, prompt, and tool changes influence product outcomes.
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 take an AI-first approach to your work, actively leveraging coding agents, LLMs, and emerging tools to accelerate analytical workflows while understanding their limitations and ensuring the accuracy and reliability of their outputs.
You've demonstrated technical leadership by setting direction on complex analytical initiatives, mentoring other data scientists, reviewing methodologies, and influencing technical standards across teams.
You combine statistical rigor with strong product and business intuition, understanding how customer engagement, AI product performance, usage-based pricing, and unit economics translate into meaningful customer and business outcomes.
You're comfortable tackling ambiguous, emerging problems where established metrics or methodologies may not yet exist, and can develop practical frameworks that balance analytical rigor with business needs.
You have a track record of influencing senior stakeholders, aligning cross-functional teams around shared definitions and priorities, and translating complex findings into clear recommendations for technical and non-technical audiences.
You're a compelling communicator who can explain sophisticated analytical concepts, challenge assumptions constructively, and communicate uncertainty and trade-offs with confidence.
You thrive in fast-moving product environments, adapt quickly as priorities evolve, and know how to balance analytical depth with the urgency and iteration required to build and improve AI-powered products.
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 data science is equally valued.
Preferred
Experience measuring and optimizing AI agents, conversational AI, or LLM-powered products in a high-growth B2B SaaS or product-focused technology company.
Hands-on experience with agent observability and evaluation platforms such as Braintrust, LangSmith, or Arize, including working with agent traces, tool-call data, and evaluation pipelines.
Experience with usage-based or consumption-based pricing, credit models, AI infrastructure costs, or LLM unit economics.
Experience building governed metrics, semantic layers, metric trees, or standardized measurement frameworks used across multiple product teams.
Experience applying classification, clustering, or other advanced analytical methods to large volumes of unstructured text, customer conversations, or agent interactions.
Experience developing internal AI agents or AI-powered analytics tools that improve team productivity and decision-making.
Familiarity with marketing attribution, incremental measurement, or evaluating the impact of AI-driven customer engagement.
Background in marketing technology, ecommerce, CRM, customer-service software, or other data-driven SaaS products.
