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Salary: USD 120,000 - 180,000 / annual
Klaviyo is seeking a Lead Customer Intelligence Analyst to join the Customer Insights & Analytics team. You will partner closely with CX stakeholders and data scientists to design new measurement systems, lead analytical investigations, and build the connected customer-intelligence layer behind Klaviyo's health scoring, personas, and experience-impact work. This role is instrumental in building—not just maintaining—the analytics infrastructure that CX leadership uses to prioritize and measure its biggest initiatives.
Key Responsibilities:
- Design and build Goals Measurement Frameworks & Benchmarks for initiatives and pilots across the CX org, providing a standard way to define, baseline, and track goal attainment across programs.
- Lead end-to-end one-off investigations: scope ambiguous business questions, pull and analyze data, and deliver decision-ready answers with minimal oversight.
- Stand up new dashboards and reporting (journey health, CX theme-and-impact, QBR/MBR inputs, NPS/OKR baselines) and corresponding documentation to allow handoffs to operational reporting teams once stable.
- Partner with data scientists on health-score and forecasting work, contributing exploratory analysis, segmentation, and validation to support modeling decisions.
- Build reusable data products (Snowflake-backed tools, automated pulls) that let insight delivery scale beyond manual reporting.
- Collaborate cross-functionally with CX leadership, data science, and operational reporting teams to keep new builds well-documented and handoff-ready.
The ideal candidate is intellectually curious, comfortable with ambiguity, and energized by building something rather than running an established process.
Requirements:
- 5–8 years of experience owning analytical problems end-to-end: from framing the business question, through analysis, to influencing stakeholders and operationalizing the solution.
- Strong, daily-use SQL and Python skills (not just familiarity).
- Track record of scoping ambiguous problems independently and driving them to a clear answer with minimal hand-holding.
- Applied statistics/ML literacy sufficient to be a credible thought partner to data scientists, without needing to own the full model lifecycle.
- Ability to communicate findings clearly to non-technical and senior stakeholders.
- Nice-to-haves: Tableau, Snowflake/Streamlit, customer analytics experience, and prior measurement framework work.
