Staff Data Scientist, Machine Learning - India

Responsibilities

• JumpCloud® is the AI-powered unified IT management platform designed to secure the modern workforce. By consolidating identity, device, and access management, JumpCloud provides intelligent, secure IT that scales from human users to autonomous AI agents. We help organizations around the globe eliminate complexity and turn AI risk into an optimized advantage, ensuring the right people and agents have secure access to the right resources at all times.
• We are seeking a hands-on Staff Data Scientist to architect and execute Machine Learning solutions, develop Large Language Models (LLMs), and build the AI stack to automate complex Data Analytics workflows. This role is at the heart of JumpCloud's applied-AI journey, combining deep foundations in classical machine learning with practical experience in building AI agents and systems that transform business decision-making and analytics delivery.
• Product-Focused ML: Experience developing real-time anomaly detection models and large-scale data processing systems to identify product usage signals and adoption patterns.
• AI Execution & Advisory: Expertise in managing AI service costs and relevance, building AI agents for automated analysis, and generating explainable insights within business workflows.
• Thought Leadership: Capability to reason through unfamiliar domains to identify potential signals, features, and modeling approaches independently.
• AI Stack Roadmap: Leading the path from opportunity sizing and prototyping through to evaluation, deployment, and impact measurement.
• Standards & Governance: Establishing pragmatic standards for model development, LLM evaluation, observability, security, and cost management.
• Cross-Functional Leadership: Aligning stakeholders with competing priorities and creating clarity in ambiguous settings to influence outcomes.
• Deep knowledge of MLOps, including feature engineering, deployment, inference, and monitoring.
• Experience building robust ML pipelines for both real-time and asynchronous predictions.

Languages