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Graph Anomaly Detection Jobs (NOW HIRING)

Delinea

Senior Software Engineer - Identity Graph

$130K - $160K/yr

... anomaly detection or risk scoring. For this Job, Delinea is not considering candidates that need any type of US work authorization now or in the future. This includes, but is not limited to: F1-OPT ...

Accroid Inc

Data Scientist

Springfield, VA · On-site

Strong foundation in Statistics, Probability, Graph Theory, or Calculus for developing and ... Experience with Signal Processing, Geospatial Analytics, Object Detection, Anomaly Detection, and ...

RIT Solutions, Inc.

AI Engineer

Vienna, VA · On-site

Required : • Solid experience in statistical modeling, clustering techniques, and probability-based analysis • Hands-on expertise in graph data analysis, including anomaly detection and ...

Blue Origin

AI Engineer III - Blue Ring

Reston, VA · On-site

... anomaly detection. Responsibilities : • Implement and optimize multi-agent systems that ... graph neural networks for network optimization or topology-aware problems • Background in model ...

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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 Aug 19, 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 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 are the key skills and qualifications needed to thrive in graph anomaly detection?

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 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 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.

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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?

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What job categories do people searching Graph Anomaly Detection jobs look for?

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Graph Anomaly Detection jobs near you
Infographic showing various Graph Anomaly Detection job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 1% As Needed, 84% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $109,451 per year, or $52.6 per hour.

Data Scientist - Sunnyvale, CA - W2 Contract

Rootshell Enterprise Technologies, Inc.

Sunnyvale, CA • On-site

Other

Re-posted 23 days ago

Job description

Job Title: Data Scientist
Location: Sunnyvale, CA
12 Months
W2 Contract
Minimum Qualifications
  • BA or BS degree in statistical analysis, computer science, data science, or related field.
  • 7+ years experience in deploying data science, machine learning, or anomaly detection techniques to solve practical business problems.
  • Outstanding written and verbal communication, with the ability to make complex data science concepts understandable to non-technical audiences.
  • Proficiency in SQL, preferably in Snowflake.
  • Experience with anomaly and outlier detection methods and algorithms.
  • Strong programming skills in Python with experience using packages such as Pandas, NumPy, scikit-learn.
  • Experience in quantitative data analysis, possessing a strong ability to conduct in-depth evaluations of complex issues.
  • Have a creative approach to engineer innovative features and signals into analytical solutions, pushing the boundaries of current tools and methodologies.
  • Applied knowledge of statistical data analysis to perform trend and anomaly identification, predictive modeling, and hypothesis testing.
  • Proven ability to make data-driven, convincing arguments to drive process changes.
  • Demonstrated experience in leading data science projects through all phases including exploratory data analysis, data quality management, modeling, tool deployment, and presentation of results.

Preferred Qualifications
  • Experience with Docker, Kubernetes, Airflow.
  • Experience with front end libraries such as React or Streamlit.
  • Experience with LLMs and Graph Databases.
  • Demonstrated ability to implement, improve, debug, and maintain machine learning models.
  • Highly Proficient in Tableau.

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