Data Analyst, User Operations

Responsibilities

🔍 Explore large support and product datasets to uncover trends, volume drivers, and user-experience pain points, distilling findings into clear, actionable narratives.
📊 Build, enhance, and maintain self-serve dashboards and reporting tools, enabling non-technical teams to answer their own data questions.
📈 Establish a unified metrics taxonomy for service-health and performance—and build automated data-sharing pipelines and scorecards with our BPO partners to ensure everyone operates from the same real-time view of success.
🛠️ Leverage LLMs to build bespoke classifiers that automatically label and segment inbound volumes—powering smarter routing, richer self-serve insights, and swifter root-cause analysis.
🤝 Partner with Data Engineering to ensure reliable pipelines, implement data-quality checks, and document sources of truth.
🚀 Jump into high-priority special projects to conduct bespoke deep-dive analyses and deliver clear, strategic recommendations to leadership.
🧪 Prototype quickly—leveraging ChatGPT, Jupyter notebooks, Retool, and other tools—to prove value before hardening with Engineering.
🤖 Collaborate with Data Science on predictive models and experimentation, translating results into operational recommendations.

8 + years in analytics, business intelligence, or data science, ideally supporting customer support or operations teams.
Expert-level SQL skills and proficiency in Python or R for advanced analysis and automation.
Hands-on experience designing and maintaining BI dashboards (e.g. Looker, Mode, Tableau, Sundial) with a focus on clarity and self-serve usability.
Hands-on experience fine-tuning or prompt-engineering LLMs to build text classifiers, sentiment analysis, or tagging systems.
Demonstrated ability to translate complex datasets into clear business stories and recommendations for both technical and non-technical audiences.
Familiarity with support metrics (SLAs, FCR, deflection) and ability to define service health KPIs.
Strong cross-functional communication skills—comfortable collaborating daily with engineers, data scientists, and operations leaders.
An eye for detail, a zero-defect mindset, and a bias toward action over theoretical perfection.
Possess a strong bias for automation and can defend governance decisions that keep data and processes healthy as they grow.
Thrive in a fast-moving, ambiguous environment where priorities can shift quickly.