Staff Data Scientist, Machine Learning - India

Job description

**All roles at JumpCloud® are Remote unless otherwise specified in the Job Description.** ### About JumpCloud® 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. ### **JumpCloud is Intelligent, Secure IT.** ### **About the Role** 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. ### What We Are Looking For: - **Professional Experience:** 8+ years across analytics, LLMs, data science, and applied machine learning, with a proven track record of owning business-facing solutions. - **Product-Focused ML:** Experience developing real-time anomaly detection models and large-scale data processing systems to identify product usage signals and adoption patterns. - **Business ML Applications:** Ability to solve business problems using predictive and prescriptive modeling, including price sensitivity, churn, revenue forecasting, and marketing mix optimization. - **Domain Expertise:** Knowledge of identity threat signals (e.g., unusual logins, MFA behavior, authentication velocity) and analyzing deviations from historical user behavior. - **AI Execution & Advisory:** Expertise in managing AI service costs and relevance, building AI agents for automated analysis, and generating explainable insights within business workflows. - **Analytics Foundations:** Strong background in segmentation, root cause analysis, funnel optimization, and acquisition quality scoring to drive growth and efficiency. - **Thought Leadership:** Capability to reason through unfamiliar domains to identify potential signals, features, and modeling approaches independently. - **Ecosystem Background:** Experience in fast-paced, data-first environments or startups where ML is a core driver of business outcomes. ### What You Will Own: - **AI Stack Roadmap:** Leading the path from opportunity sizing and prototyping through to evaluation, deployment, and impact measurement. - **Technical Execution:** Ensuring robust performance, validation, and inference of real-time models using the latest industry improvements. - **Standards & Governance:** Establishing pragmatic standards for model development, LLM evaluation, observability, security, and cost management. - **Capability Mentorship:** Coaching analysts and creating reusable tools to raise the collective quality of predictive problem-solving. - **Strategic Communication:** Translating complex technical work into clear recommendations and trade-offs for senior stakeholders. - **Cross-Functional Leadership:** Aligning stakeholders with competing priorities and creating clarity in ambiguous settings to influence outcomes. ### Technical Breadth: - Expertise in Python, SQL, statistical analysis, and common ML frameworks. - Deep knowledge of MLOps, including feature engineering, deployment, inference, and monitoring. - Proficiency with LLM APIs, RAG, Semantic Layers, Embeddings, and agent orchestration frameworks. - Experience building robust ML pipelines for both real-time and asynchronous predictions. ### Behavioral Strengths - Exceptional business acumen and clarity of thought. - Curiosity driven by purpose with the ability to "zoom in" on details and "zoom out" for strategy. ### Preferred Qualifications - Understanding of recurring-revenue B2B SaaS business models. - Experience with Data Engineering: Data governance, transformation, and orchestration patterns on modern platforms.