Principal Data Scientist

** Interested candidates, please email your latest resume to [email protected] Principal Data Scientist for our 0-1 AI-native data infrastructure platform. You are the senior-most IC in the founding pod. You will not manage people, but you will own the most ambiguous, complex technical problems and set the technical bar for the entire function.You are the go-to person when a problem has no clear solution. You will lead critical 0-1 initiatives from whiteboard to production and mentor other scientists through deep technical guidance and code/design reviews.What You Will Own:0-1 Technical Leadership: Lead end-to-end delivery of core AI-native platform capabilities - e.g., LLM agents for data workflows, semantic search & discovery, auto-optimization, data quality anomaly detection.Architecture & Design: Make foundational architecture decisions for our ML / GenAI stack. Define patterns for RAG, evaluation, guardrails, and LLMOps that the rest of the team will follow.Production Excellence: Build robust, scalable, production-grade models and systems. Own model lifecycle: feature engineering, training, evaluation, deployment, monitoring, and iteration.Cross-Functional Influence: Partner directly with Product, Data Engineering, and Founders to translate vague product bets into shippable data science solutions.Raise the Bar: Set standards for code quality, experimentation rigor, documentation, and MLOps. Mentor Senior / Mid-level scientists.Who You Are:7-10+ years of hands-on experience, with 2+ years as a Staff / Lead / Principal Data Scientist in a top product company or high-growth startup.Proven 0-1 builder: You have taken at least one ML/GenAI product from 0 -> 1 -> scale in production. Data Infra / B2B SaaS / AI Platform experience is a huge plus.Expert in Python, SQL, and modern ML stack. Deep expertise in at least 2 of: LLMs / RAG / AI Agents, NLP, Knowledge Graphs / Semantic Layer, Time-series / Anomaly Detection, Recommenders.Strong system design: You think beyond models - you design for latency, cost, scalability, and maintainability. Hands-on with Spark, vector DBs, orchestration, feature stores, MLflow.** Interested candidates, please email your latest resume to [email protected]