Customer Success Data Analyst - AI/Automation

Qlik•Boston, MA

2d•$80,000 - $85,000

About The Position

Step into a pivotal position as a Data Analyst with an AI/Automation specialization within our Customer Success Organization’s Business Operations and Insights team. Your experience in data analysis and process optimization will directly contribute to shaping strategies that enhance our customers’ experience and drive organizational success. This role offers an exciting opportunity to work with cross-functional teams, dive deep into data storytelling, and create actionable insights that truly make a difference.

Requirements

A background that sets you up for success: A bachelor’s degree (or equivalent experience) in Business, Computer Science, Math or Data Science.
Proven experience: 2+ years in data analysis preferably in a SaaS, B2B technology or Customer Success environment.
Analytics Tool Proficiency: Proficiency with Qlik Sense or similar tools (SQL, Tableau, PowerBI), plus advanced Excel skills.
Strong Data Foundations: Strong SQL skills and a basic understanding of Python, APIs or LLM architecture.
AI/Automation experience: Experience with AI prompt engineering or no-code/low-code automations to improve outputs from existing processes.
Outcome-focused: Demonstrated ability to drive innovative change and achieve positive business outcomes through data analysis and insights.
Communication skills that shine: The ability to clearly present insights to both technical and non-technical stakeholders.

Responsibilities

Generate and analyze key performance indicators (KPIs) that measure and drive operational effectiveness, business performance and efficiency.
Support engagement and retention strategies by delivering accurate forecasting and tailored customer insights.
Prepare regular reports, dashboards, and presentations to communicate insights and recommendations to management and stakeholders.
Expand the Customer Success AI prompt library by developing, refining or optimizing reusable inputs for generative AI models .
Identify areas for improvement in existing processes and creating related no-code/low-code automation workflows to achieve scalability and predictability of outputs.