Data Scientist - Agentic AI Systems - IFS Loops

Data Scientist - Agentic Ai Systems - Ifs LoopsWe are seeking a data scientist with a strong research mindset to help shape the future of agentic AI systems. This role blends deep analytical thinking with fast-paced experimentation and model development, where your insights will directly inform how AI agents reason, plan, and act in dynamic environments. You'll work on a cross-functional team exploring how AI agents learn from real-world data, adapt to user intent, and interact autonomously—or collaboratively—with users and systems. If you're a self-starter who thrives on ambiguity, builds fast, and can bridge theory with practical outcomes, this role is for you. Key responsibilities include designing and running applied research initiatives that inform the behavior and learning loops of AI agents, building and evaluating models for planning, memory, retrieval, reasoning, or tool use in agentic systems, developing internal tools and pipelines to test agent behavior across different environments, analyzing structured and unstructured data from user-agent interactions, logs, and experiments, rapidly prototyping and testing hypotheses to improve agent performance and reliability, collaborating with engineering, product, and design to translate insights into deployable features, and communicating findings clearly and concisely to both technical and non-technical audiences. Minimum qualifications include a masters or PhD in computer science, applied math, statistics, or related quantitative field, 4+ years of experience in data science, applied ML, or AI research, strong Python and SQL skills; experience with libraries like scikit-learn, PyTorch, LangChain or any similar agentic framework, familiarity with LLMs, retrieval-augmented generation (RAG), reinforcement learning, fine-tuning, comfort with ambiguity, fast iteration cycles, and self-directed research, and excellent communication and storytelling skills — you can explain complex models to others clearly. Preferred qualifications include experience working with agent frameworks (AutoGen, OpenAgents, LangGraph, etc.), background in decision-making models, memory systems, or multi-agent coordination, exposure to vector databases, embeddings, and custom RAG pipelines, experience building evaluation frameworks or simulators for agent performance, and experience with LLM post training – SFT, DPO, RLHF, GRPO.