Chief Data Architect

4 days ago


nellore, India beBeeAI Full time

Senior Data ScientistWe are building an AI system to accelerate teamwork for software teams.The founding AI/ML engineer will own the intelligence layer of the product, focusing on practical, real-world AI/ML with a strong emphasis on correctness, latency, and reliability.Cross-Channel Communication Understanding: Build systems that understand and classify content across various channels into actionable tasks, status checks, meeting requests, information, or noise.Task Domain Identification: Determine the domain of each task based on message content and context.Work Decomposition: Infer the actual work needed by taking vague task requests and converting them into structured, actionable work units.Agent Routing & Orchestration: Trigger internal or external agents to help complete tasks, with human confirmation when required.Project/Topic Mapping: Cluster related messages across channels into implicit 'Projects' without requiring manual creation.Autonomous fetch of latest Status: Pull data from various sources, including Slack threads, Jira/Linear, Git/GitHub/Bitbucket, and prepare accurate updates for user approval.Scheduling Intelligence: Detect meeting-related asks, identify participants, find availability, and propose times.About this RoleThis is a unique opportunity to join our team as a Senior Data Scientist and drive innovation in AI-powered teamwork. As the founding AI/ML engineer, you will play a crucial role in shaping the intelligence layer of our product.We are looking for someone with expertise in developing practical, real-world AI/ML solutions with a strong emphasis on correctness, latency, and reliability.Your key responsibilities will include:Designing and implementing AI systems that understand and classify content across various channelsDetermining the domain of each task based on message content and contextInfering the actual work needed by taking vague task requests and converting them into structured, actionable work unitsTriggering internal or external agents to help complete tasks, with human confirmation when requiredClustering related messages across channels into implicit 'Projects' without requiring manual creationPulling data from various sources, including Slack threads, Jira/Linear, Git/GitHub/Bitbucket, and preparing accurate updates for user approvalDetecting meeting-related asks, identifying participants, finding availability, and proposing times



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