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Graph Anomaly Detection Jobs (NOW HIRING)

Appgate

Staff Machine Learning Engineer

Manhattan, NY · On-site

$180K - $220K/yr

Familiarity with streaming analytics , graph ML , or time-series anomaly detection . * Knowledge of model governance , bias mitigation , and regulatory compliance in fraud contexts. * Contributions ...

Appgate

Principal Machine Learning Engineer

Manhattan, NY · On-site

$220K - $265K/yr

Familiarity with streaming analytics , graph ML , or time-series anomaly detection . * Knowledge of model governance , bias mitigation , and regulatory compliance in fraud contexts. * Contributions ...

Guidehouse

AI/ML Software Engineer

$117K - $140K/yr

Develop machine learning models for risk scoring, anomaly detection, classification, clustering, and predictive analytics to support investigative and operational decision-making. * Apply graph ...

Illumio

Sr. Security Data Scientist

Sunnyvale, CA · On-site

$170K - $196K/yr

This graph enables essential functions such as breach risk detection, network segmentation ... Create ML models for anomaly detection, behavioral profiling, and breach identification across ...

DAT

Senior Applied Scientist, Trust & Safety

Seattle, WA

$104K - $142K/yr

Apply machine learning, graph and network algorithms, anomaly detection, and other quantitative methods to deliver measurable improvements in fraud prevention and operational effectiveness. * Take ...

System One

AI/ML Engineer

Mclean, VA · On-site

Develop machine learning models for classification, clustering, anomaly detection, risk scoring, predictive analytics, and pattern recognition. * Apply graph analytics, network analysis, and link ...

DAT

Senior Applied Scientist, Trust & Safety

Seattle, WA · On-site

$104K - $142K/yr

Apply machine learning, graph and network algorithms, anomaly detection, and other quantitative methods to deliver measurable improvements in fraud prevention and operational effectiveness. * Take ...

System One Holdings, LLC

AI/ML Engineer

Mclean, VA · On-site

Develop machine learning models for classification, clustering, anomaly detection, risk scoring, predictive analytics, and pattern recognition. * Apply graph analytics, network analysis, and link ...

System One

AI/ML Engineer

Mclean, VA · Remote

Develop machine learning models for classification, clustering, anomaly detection, risk scoring, predictive analytics, and pattern recognition. * Apply graph analytics, network analysis, and link ...

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All JobsGraph Anomaly Detection Jobs

Graph Anomaly Detection information

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$54K

$109.5K

$161.5K

How much do graph anomaly detection jobs pay per year?

As of Jul 27, 2026, the average yearly pay for graph anomaly detection in the United States is $109,451.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,000.00 and $123,000.00 per year, depending on experience, location, and employer.

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.

More about Graph Anomaly Detection jobs
What cities are hiring for Graph Anomaly Detection jobs? Cities with the most Graph Anomaly Detection job openings:
What states have the most Graph Anomaly Detection jobs? States with the most job openings for Graph Anomaly Detection jobs include:
What job categories do people searching Graph Anomaly Detection jobs look for? The top searched job categories for Graph Anomaly Detection jobs are:
Graph Anomaly Detection jobs near you
Infographic showing various Graph Anomaly Detection job openings in the United States as of July 2026, with employment types broken down into 3% As Needed, 94% Full Time, 1% Part Time, and 2% Contract. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution, with an average salary of $109,451 per year, or $52.6 per hour.
Data Scientist / Graph AI Engineer

Data Scientist / Graph AI Engineer

Programmers.io

Austin, TX • On-site

Temporary

Posted 5 days ago

Job description

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 novel 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, Protégé).
  • 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, novel 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.