About the Role:
As a Senior Data Analyst, you will be responsible for preparing, analyzing, and interpreting large datasets to support the development and evaluation of machine learning models. Your work will focus on ensuring high-quality data, identifying meaningful patterns, and generating actionable insights that contribute to AI-driven solutions. This role demands strong analytical skills, attention to detail, and a keen interest in working with machine learning data in a dynamic, fast-paced environment.
Responsibilities
Own enterprise client onboarding for agentic AI implementations, including data analysis, topic clustering, coverage planning, and workflow/action design
Design, implement, and optimize prompts, conversation flows, agent logic, and knowledge configurations for production AI agents
Deliver AI solutions end-to-end, from solution design through UAT, production launch, and post-go-live optimization
Own post-launch AI performance at the client level, driving improvements in containment, resolution quality, and handoff reduction
Analyze conversation data, quality signals, and DSAT drivers to identify root causes and optimization opportunities
Implement iterative improvements across prompts, workflows, actions, guardrails, and knowledge bases based on data and evaluation results
Collaborate with AI Quality & Evaluation teams to validate response accuracy, conversation quality, and regression risk
Partner with Client Analytics and ML-Ops teams to act on insights, platform changes, and production issues
Mentor junior analysts and engineers through design reviews, solution feedback, and applied AI best-practice guidance
Requirement
5+ years of experience in applied AI, conversational AI, analytics, or AI-powered customer experience solutions
Hands-on experience with LLMs, prompt engineering, agent workflows, and tool/action-based AI systems
Experience working with real-world customer interaction data (chat, tickets, email, call transcripts, or voice data)
Strong analytical ability to diagnose and resolve AI behavior issues across prompts, knowledge, workflows, evaluation, and user experience
Working knowledge of SQL and Python for data analysis, experimentation, and debugging AI behavior
Ability to use Tableau, Power BI, or similar BI tools to analyze trends, quality signals, and performance metrics
Experience collaborating with cross-functional teams including analytics, quality, product, and ML-Ops
Comfortable working directly with enterprise customers and translating ambiguous requirements into clear, scalable AI solutions
