Data Scientist - Agentic AI Systems - Loops

Data Scientist
- Agentic AI Systems
- Loops

• Full-time

IFS is a billion-dollar revenue company with 7000+ employees on all continents. Our leading AI technology is the backbone of our award-winning enterprise software solutions, enabling our customers to be their best when it really matters–at the Moment of Service™. Our commitment to internal AI adoption has allowed us to stay at the forefront of technological advancements, ensuring our colleagues can unlock their creativity and productivity, and our solutions are always cutting‑edge.

At IFS, we’re flexible, we’re innovative, and we’re focused not only on how we can engage with our customers but on how we can make a real change and have a worldwide impact. We help solve some of society’s greatest challenges, fostering a better future through our agility, collaboration, and trust.

We celebrate diversity and understand our responsibility to reflect the diverse world we work in. We are committed to promoting an inclusive workforce that fully represents the many different cultures, backgrounds, and viewpoints of our customers, our partners, and our communities. As a truly international company serving people from around the globe, we realize that our success is tantamount to the respect we have for those different points of view.

By joining our team, you will have the opportunity to be part of a global, diverse environment; you will be joining a winning team with a commitment to sustainability; and a company where we get things done so that you can make a positive impact on the world.

We’re looking for innovative and original thinkers to work in an environment where you can #Make Your Moment so that we can help others make theirs. With the power of our AI‑driven solutions, we empower our team to change the status quo and make a real difference.

If you want to change the status quo, we’ll help you make your moment. Join Team Purple. Join IFS.

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.

• 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

• 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, Lang Chain 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

• Experience working with agent frameworks (Auto Gen, Open Agents, Lang Graph, 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