Data Scientist

Title- Data Scientist (Agentic AI) Location- Plano, TX- Fully Onsite from Day-1 W2 position, 40/hr on W2 Must Need Agentic Experience Job Description- As a Data Scientist specializing in Agentic AI and language graphs, you will design, develop, and optimize autonomous AI agents capable of complex reasoning and decision-making. You will leverage advanced language graph models to represent, analyze, and generate structured knowledge that enhances agent capabilities. This role requires a blend of strong data science skills, natural language understanding, graph theory, and AI research experience. Key Responsibilities: Develop and implement Agentic AI models that enable autonomous agents to perform goal-directed tasks with minimal supervision. Design and maintain sophisticated language graph structures that represent semantic knowledge, relationships, and context from large-scale text data. Apply graph neural networks (GNNs), knowledge graph embeddings, and natural language processing (NLP) techniques to improve agent comprehension and reasoning. Collaborate with cross-functional teams including AI researchers, software engineers, and product managers to integrate language graph-based Agentic AI into scalable applications. Conduct exploratory data analysis and feature engineering on large, heterogeneous datasets to support model training and evaluation. Experiment with and refine reinforcement learning, planning algorithms, and multi-agent coordination to enhance agent autonomy. Develop metrics and evaluation frameworks to measure agent performance, accuracy, and robustness. Stay current with the latest research in Agentic AI, graph-based NLP, and related fields, and apply best practices to advance our technology. Document methodologies, processes, and findings to facilitate knowledge sharing and reproducibility. Qualifications: Proven experience in developing Agentic AI systems or autonomous AI agents. Strong expertise in language graphs, knowledge graphs, or semantic graph representations. Proficiency with graph databases and frameworks (e.g., Neo4j, DGL, PyTorch Geometric). Solid background in NLP techniques including transformers, language models, and embedding methods. Experience with graph neural networks and advanced graph algorithms. Programming proficiency in Python and relevant AI/ML libraries Familiarity with reinforcement learning and planning algorithms is a plus. Strong analytical, problem-solving, and communication skills. Ability to work collaboratively in a fast-paced, innovative environment.