Sr Analyst, Customer Intelligence

Avalara

15h•$111,300 - $215,400•Remote

About The Position

Avalara is building an AI-first operating model to predict customer needs, reduce friction, and improve gross revenue retention. Fragmented reporting and inconsistent scoring currently limit early detection of renewal risk and high-impact friction. The Lead Analyst, Customer Intelligence owns the analytical foundation behind CX360, Avalara’s enterprise customer intelligence system. This role defines and governs scoring frameworks, driver methodologies, predictive indicators, and prioritization models that convert multi-source customer signals into structured, decision-grade insight. This role directly enables measurable improvement in GRR, CPO, CSAT, CES, CPC, TTR, case deflection, renewal risk detection, expansion identification, and churn probability modeling. Business: Establishes a governed, enterprise-standard intelligence system that improves retention, reduces cost-to-serve, and strengthens roadmap and operational decisions. Customer: Enables earlier detection of lifecycle friction and renewal risk, allowing proactive intervention before issues escalate. Organization: Raises analytical rigor and AI capability by standardizing scoring frameworks and embedding decision-grade intelligence into enterprise workflows.

Requirements

Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering.
6+ years of progressive experience in advanced analytics, customer intelligence, enterprise BI, or CX measurement roles within SaaS or complex customer environments.
Demonstrated experience designing and governing scoring frameworks, driver analysis models, or prioritization methodologies tied to measurable revenue and retention outcomes.
Proven experience building and deploying predictive models (e.g., churn probability, renewal risk, expansion propensity) that informed real business decisions.
Demonstrated experience operationalizing AI- and LLM-driven analytics within enterprise decision workflows — not just experimentation or prototype analysis.
Experience applying NLP and machine learning techniques to large-scale unstructured customer feedback and behavioral datasets.
Experience partnering with Data Science, Engineering, Product, and Finance to move models from concept to production deployment.
Strong experience designing executive-facing data visualizations and translating statistical findings into prioritized, decision-ready recommendations.
Demonstrated ability to connect customer experience and analytical outputs directly to GRR, CPO, CSAT, operational efficiency, and cost-to-serve metrics.
Experience operating as a senior-level individual contributor, setting analytical standards and influencing cross-functional direction without direct authority.
You’ll bring experience using AI and AI-related technologies, ready to thrive here.
You’ll apply AI every day to business challenges - improving efficiency, contributing solutions, and driving results for your team, our company, and our customers.
You’ll grow with AI by staying curious about new trends and best practices, and by sharing what you learn so others can benefit too.

Responsibilities

Ensure CX360 delivers accurate, trusted, decision-ready customer intelligence across Product, CX, Operations, and executive leadership, with clear ownership of scoring integrity.
Define and govern scoring frameworks, lifecycle metrics, friction models, and prioritization thresholds to ensure consistency, transparency, and business relevance.
Collaborate with Data Science, Architecture, Engineering, and Product to validate data integrity and strengthen insights through predictive modeling and sentiment analysis.
Build and evolve customer intelligence analytics including segmentation, cohort analysis, and driver attribution tied directly to retention and efficiency outcomes.
Quantify the financial and operational impact of customer friction using revenue and cost modeling to establish measurable prioritization standards.
Translate complex analytics into structured, prioritized recommendations that influence retention strategy, roadmap investment, and operational optimization.
Apply AI techniques — including NLP, LLM integrations, and predictive models — to automate feedback classification, improve signal detection, and reduce time-to-insight.
Maintain governed analytics environments across Qualtrics, Snowflake, Power BI, Tableau, and related systems to ensure secure, scalable enterprise intelligence.
Establish CX360 as the enterprise-standard source of customer health and renewal risk intelligence.
Standardize scoring and prioritization frameworks to eliminate metric drift and fragmented reporting.
Deploy at least one productionized predictive model influencing planning or roadmap decisions.
Demonstrate measurable movement in retention, efficiency, or cost-to-serve tied to validated friction interventions.
Reduce manual analysis and reporting redundancy through AI-enabled automation.
Increase executive confidence in customer metrics through documented governance and scoring transparency.
Set and enforce high standards for analytical rigor, scoring integrity, and metric governance.
Challenge assumptions that weaken decision quality or dilute customer impact.
Improve how teams apply customer intelligence in strategic decisions.
Leave intelligence systems and frameworks stronger than you found them.
Embed AI into core analytics workflows as production capability, not experimentation.
Quantify measurable business impact from AI-enabled intelligence (e.g., improved predictive accuracy, reduced cycle time, increased automation).
Identify high-impact AI applications tied to revenue protection, operational efficiency, or customer experience.
Partner cross-functionally to deploy AI responsibly and at scale.