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Graph Anomaly Detection Jobs in Texas (NOW HIRING)

... knowledge graph technologies. This position will be based full time in either Westlake, TX or ... anomaly detection. The successful candidate must be comfortable operating in a fast-paced and ...

... anomaly detection โ€ข Cassandra(1+), Kafka(1+), Apollo Graph QL(1+) Qualifications : Required : โ€ข 10+ years of backend engineering experience with strong Java fundamentals โ€ข Hands-on development ...

Senior Machine Learning Engineer

Houston, TX ยท On-site

$99K - $137K/yr

Hands-on experience with CNN, RNN, Graph Neural Networks, and transformers. * Proficiency in ... Expertise in classification, regression, anomaly detection, and sequence modeling. * Practical ...

Advanced Anomaly Detection with Graph: Track record developing hybrid graph-temporal approaches (e.g., GNN + Transformer, graph contrastive learning, dynamic graph forecasting) for detecting ...

Advanced Anomaly Detection with Graph: Track record developing hybrid graph-temporal approaches (e.g., GNN + Transformer, graph contrastive learning, dynamic graph forecasting) for detecting ...

Advanced Anomaly Detection with Graph: Track record developing hybrid graph-temporal approaches (e.g., GNN + Transformer, graph contrastive learning, dynamic graph forecasting) for detecting ...

Senior Staff Agentic AI Engineer

Frisco, TX ยท On-site +1

$99K - $134K/yr

... anomaly detection, and decisioning. The role includes implementing deterministic paths where needed, integrating graph/RAG memory, and establishing strong observability and evaluation pipelines. You ...

Leverage AI tooling to automate identity operations, access reviews, and anomaly detection where ... Develop API-driven integrations (Microsoft Graph, AWS APIs, REST) for cross-application ...

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Graph Anomaly Detection information

What are common challenges faced by professionals working in Graph Anomaly Detection, and how can they be addressed?

Professionals in Graph Anomaly Detection often face challenges such as handling large-scale, high-dimensional graph data and distinguishing between legitimate anomalies and noise. Collaborating closely with data engineers and domain experts is essential to ensure accurate labeling and effective feature engineering. Additionally, staying updated with rapidly evolving algorithms and tools is important for optimizing detection performance. Regular cross-functional meetings and ongoing training can help address these challenges and support continuous improvement.

What are the key skills and qualifications needed to thrive in Graph Anomaly Detection, and why are they important?

To excel in Graph Anomaly Detection, you need strong skills in data science, graph theory, and machine learning, typically supported by a degree in computer science or a related field. Proficiency with Python, libraries like NetworkX or PyTorch Geometric, and familiarity with graph databases such as Neo4j are commonly required. Analytical thinking, problem-solving abilities, and attention to detail help professionals identify subtle anomalies and communicate findings effectively. These skills ensure accurate detection of unusual patterns, supporting cybersecurity, fraud prevention, and other critical applications.

What is graph anomaly detection?

Graph anomaly detection is the process of identifying unusual patterns, nodes, edges, or substructures within graph data that deviate from expected behavior. These anomalies could indicate fraud, security breaches, network failures, or other significant events in applications such as cybersecurity, social networks, and financial transactions. Techniques in graph anomaly detection leverage statistical, machine learning, and deep learning approaches to analyze complex relationships and structures within graph data. Detecting these anomalies helps organizations quickly address potential threats or irregularities. The field is interdisciplinary, combining knowledge from data science, graph theory, and domain-specific expertise.

What is the difference between Graph Anomaly Detection vs Data Scientist?

AspectGraph Anomaly DetectionData Scientist
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of graph theory and machine learningDegree in Statistics, Computer Science, or related fields; proficiency in programming and data analysis
Work EnvironmentResearch labs, tech companies, industries analyzing network dataBusiness, finance, tech firms analyzing large datasets for insights
Industry UsageSpecialized in detecting irregularities in graph-structured dataBroadly used for data analysis, predictive modeling, and decision-making

While both roles involve data analysis and machine learning, Graph Anomaly Detection focuses specifically on identifying irregularities within graph-structured data, whereas Data Scientists work across various data types and analytical tasks. Understanding these differences helps organizations choose the right expertise for their data challenges.

What cities in Texas are hiring for Graph Anomaly Detection jobs? Cities in Texas with the most Graph Anomaly Detection job openings:
Data Scientist / Graph AI Engineer

Data Scientist / Graph AI Engineer

Programmers.io

Austin, TX โ€ข On-site

Temporary

Posted 7 days ago


Job description

Job Description

Overview
We are seeking a Data Scientist / Graph AI Engineerย 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 novel methodologies to transform machine log data into ontology-driven semantic graphsย that enable clustering, anomaly detection, and downstream analytics.
This role demands a thinker, builder, and innovatorย 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:ย Strong background in graph databases (Neo4j, TigerGraph), graph processing (NetworkX, DGL, PyTorch Geometric), and ontology modeling (OWL, RDF, Protรฉgรฉ).
  • Machine Learning:ย Proven experience with graph embeddings, anomaly detection, clustering, and time-series analysis.
  • AI/LLM Innovation:ย Hands-on experience applying or extending large language modelsย for data representation, semantic reasoning, or code generation.
  • Programming & Engineering:ย Advanced skills in Python, PyTorch/TensorFlow, Spark, and cloud-native pipelines.
  • Research & IP Creation:ย Track record of innovation (patents, publications, novel algorithms).
  • Communication:ย Ability to engage stakeholders with clarity, empathy, and influence
  • Experience with Splunk log dataย or similar enterprise log platforms.
  • Familiarity with graph-based anomaly detection benchmarksย and scalable ML infrastructure.