Senior Data Analyst
New
N
NetomiArtificial Intelligence
GurugramFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLData AnalysisMachine LearningMicrosoft Power BITableauPrompt Engineering
Requirements
- 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.
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.
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