Lead Success Operations Analyst

Klaviyo is looking for a Lead Customer Intelligence Analyst to join our growing Customer Insights & Analytics team. In this role, you will partner closely with CX stakeholders and data scientists to design new measurement systems, lead ambiguous analytical investigations, and help 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.

The ideal candidate is intellectually curious, comfortable with ambiguity, and energized by building something rather than running an established process.

How you will make a difference:

• Design and build the 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 — scoping an ambiguous business question, pulling and analyzing the data, and delivering a decision-ready answer 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.

Who you are:

• 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.

• You have strong, daily-use SQL and Python skills, not just familiarity.

• You have a track record of scoping ambiguous problems yourself and driving them to a clear answer, with minimal hand-holding.

• You have enough applied statistics/ML literacy to be a credible thought partner to a data scientist, without needing to own the full model lifecycle.

• You communicate findings clearly to non-technical and senior stakeholders.

• Tableau, Snowflake/Streamlit, customer analytics experience, and prior measurement framework work is a plus.