Principal Data Scientist - Agentic AI & Applied Intelligence

We are seeking a highly experienced Principal Data Scientist with 10+ years of experience in Data Science, Machine Learning, and AI systems development, including recent hands-on expertise in Agentic AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and enterprise AI platforms.

This role will lead the design and evaluation of intelligent AI systems that combine machine learning, statistical modeling, retrieval systems, reasoning frameworks, and agent orchestration to solve complex business problems. The ideal candidate can operate at both the strategic and technical levels, driving AI innovation while remaining deeply hands-on in experimentation, evaluation, architecture, and production deployment.

Key Responsibilities

Agentic AI & LLM Systems

• Design and develop multi-agent AI systems using frameworks such as LangGraph, Semantic Kernel, CrewAI, AutoGen, ADK, or custom orchestrators.
• Build planner-executor architectures, tool-using agents, memory-enabled agents, and agent collaboration workflows.
• Design agent routing, state management, agent evaluation, and human-in-the-loop workflows.
• Create enterprise-grade AI assistants, copilot solutions, and decision-support systems.

Data Science & Modeling

• Lead development of predictive models across classification, forecasting, optimization, anomaly detection, recommendation systems, and risk analytics.
• Design experimentation frameworks including A/B testing, causal inference, uplift modeling, and statistical validation methodologies.
• Build interpretable machine learning systems and define KPIs that directly align with business outcomes.
• Develop advanced feature engineering strategies for structured and unstructured enterprise datasets.

Retrieval & Knowledge Systems

• Architect RAG solutions using:
• Hybrid retrieval
• Semantic search
• Knowledge graphs
• GraphRAG
• Vector databases
• Re-ranking architectures
• Design chunking, embedding, indexing, and retrieval optimization strategies.
• Develop retrieval evaluation frameworks using Recall@K, Precision@K, MRR, NDCG, and human evaluation.

AI Evaluation & Governance

• Design evaluation frameworks for:
• Hallucination detection
• Groundedness
• Faithfulness
• Agent reliability
• Tool-calling accuracy
• Task completion success
• Develop golden datasets and benchmark suites.
• Implement AI governance frameworks supporting model risk management, explainability, compliance, and auditability.

Leadership

• Mentor Data Scientists, Applied Scientists, and ML Engineers.
• Guide architecture reviews and technical design decisions.
• Partner with Product, Engineering, Legal, Compliance, and Executive Leadership.
• Drive AI roadmap development and platform adoption.

• Master’s or PhD in Computer Science, Data Science, Statistics, Applied Mathematics, Engineering, or related field.
• 10+ years of experience in Data Science and Machine Learning.
• 3+ years building production GenAI systems.
• Expertise in:
• Python
• SQL
• PySpark
• Scikit-Learn
• XGBoost
• Deep Learning frameworks
• Experience with:
• LangChain
• LangGraph
• LlamaIndex
• RAG
• Multi-agent architectures
• Model evaluation frameworks
• Strong understanding of:
• Statistics
• Hypothesis testing
• Experimental design
• Causal inference, * Production experience with GraphRAG.
• Experience with MCP (Model Context Protocol).
• Experience with A2A (Agent-to-Agent communication).
• Publications, patents, or conference speaking experience.
• Experience in regulated industries such as healthcare, finance, insurance, or legal.