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We're seeking our first Product Data Analyst to establish data-driven product insights for Maisa Studio. As the founding data hire, you'll build the analytics foundation from the ground up, creating the metrics, dashboards, and analytical frameworks that will guide product decisions for our AI-powered automation platform. This role requires deep understanding of AI product analytics, user behavior in complex enterprise software, and the unique challenges of measuring success in agentic AI systems. You'll work directly with our distributed microservices architecture, analyzing everything from Digital Worker performance to user engagement patterns, helping us understand how enterprises adopt and succeed with AI automation.
Job Responsibility:
Design and implement comprehensive product analytics framework for Maisa Studio
Establish key product metrics, KPIs, and success measurements for AI Digital Workers
Build data pipelines and dashboards to track user behavior, worker performance, and platform adoption
Create the foundational data infrastructure to support product, engineering, and business decisions
Define data governance and quality standards for product analytics
Analyze Digital Worker creation, configuration, and deployment patterns
Track AI reasoning quality, execution success rates, and error patterns across the KPU system
Measure user adoption of different AI tools, integrations, and workflow configurations
Analyze the effectiveness of our "Chain of Work" traceability and explainability features
Monitor AI model performance, token usage, and computational efficiency across workers
Analyze user journeys through Maisa Studio interface and identify friction points
Track feature adoption, time-to-value, and user engagement patterns
Study how different user personas (business users vs. technical users) interact with the platform
Analyze worker sharing, collaboration, and deployment success patterns
Identify usage patterns that correlate with customer success and expansion
Monitor and analyze system performance metrics across microservices architecture
Track API usage patterns, response times, and bottlenecks through Kong Gateway
Analyze resource utilization across Code Engine, Marathon system, and execution environments
Monitor data flow patterns through Apache Kafka and identify system optimization opportunities
Analyze file storage patterns, processing times, and temporary data lifecycle efficiency
Track onboarding funnels and time-to-first-worker-deployment
Analyze enterprise usage patterns, worker complexity evolution, and scaling behaviors
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