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# Data Scientist - Agentic AI Systems - IFS Loops

- **Company:** IFS
- **Location:** Palo Alto, CA, United States
- **Experience:** Experienced
- **Salary:** $140,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Performance Tuning, Standard Sql, Unstructured Data, Reinforcement Learning, Pytorch, Large Language Models, Multi-Agent Systems, Generative AI, Scikit Learn, Information Technology, Virtual Agents
- **Published:** August 9, 2026
- **Apply:** https://www.wayup.com/i-j-Data-Scientist-Agentic-AI-Systems-IFS-Loops-IFS-437178656277000/

## About the Role

+ 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
 + Excellent communication and storytelling skills - you can explain complex models to others clearly
** Preferred Qualifications**

 + 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
 + Experience with LLM Post Training - SFT, DPO, RLHF, GRPO
** Who You Are**

 + Curious - You explore the edges of what's possible with AI agents
 + Action-biased - You ship prototypes quickly and iterate based on evidence

## Description

We 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

 + Design and run applied research initiatives that inform the behavior and learning loops of AI agents
 + Build and evaluate models for planning, memory, retrieval, reasoning, or tool use in agentic systems
 + Develop internal tools and pipelines to test agent behavior across different environments
 + Analyze structured and unstructured data from user-agent interactions, logs, and experiments
 + Rapidly prototype and test hypotheses to improve agent performance and reliability
 + Collaborate with engineering, product, and design to translate insights into deployable features
 + Communicate findings clearly and concisely to both technical and non-technical audiences


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