Data Scientist / Graph AI Engineer

Job Description

Overview

We are seeking a Data Scientist / Graph AI Engineer with deep expertise in semantic graph analytics, AI-driven anomaly detection, and large language models (LLMs). This individual will serve as a technical pioneer, designing, implementing, and validating Client methodologies to transform machine log data into ontology-driven semantic graphs that enable clustering, anomaly detection, and downstream analytics.

This role demands a thinker, builder, and innovator who thrives in customer-centric environments, can invent intellectual property, and can navigate the intersection of data engineering, graph representation learning, and AI / LLM-based methodology creation.

Required Skills & Experience

• Graph Expertise : Strong background in graph databases (Neo4j, TigerGraph), graph processing (NetworkX, DGL, PyTorch Geometric), and ontology modeling (OWL, RDF, Protege).
• Machine Learning : Proven experience with graph embeddings, anomaly detection, clustering, and time-series analysis.
• AI / LLM Innovation : Hands-on experience applying or extending large language models for data representation, semantic reasoning, or code generation.
• Programming & Engineering : Advanced skills in Python, PyTorch / TensorFlow, Spark, and cloud-native pipelines.
• Research & IP Creation : Track record of innovation (patents, publications, Client algorithms).
• Communication : Ability to engage stakeholders with clarity, empathy, and influence
• Experience with Splunk log data or similar enterprise log platforms.
• Familiarity with graph-based anomaly detection benchmarks and scalable ML infrastructure.

Diverse Lynx LLC is an Equal Employment Opportunity employer. All qualified applicants will receive due consideration for employment without any discrimination. All applicants will be evaluated solely on the basis of their ability, competence and their proven capability to perform the functions outlined in the corresponding role. We promote and support a diverse workforce across all levels in the company.