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Graph Anomaly Detection Jobs (NOW HIRING)

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 ...

Nexwave

Sr. Data Scientist

Minnetonka, MN · On-site

$70/hr

Experience with machine learning, anomaly detection, pattern recognition, predictive modeling ... Experience with advanced modeling approaches such as ensemble methods, deep learning, graph ...

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 ...

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 ...

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 ...

Illumio

Director, Product Management

Sunnyvale, CA · On-site

$267K - $320K/yr

Understanding of AI/ML technologies applied to security detection - anomaly detection algorithms, graph neural networks, or LLM-based security reasoning. * Background in network forensics, incident ...

Rain

Machine Learning Engineer - Fraud Risk

New York, NY · On-site

$170K - $240K/yr

Architect and build scalable ML systems for fraud detection, anomaly detection, and behavioral ... Experience with graph-based or network-level fraud detection techniques * A graduate degree in ...

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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 Sep 12, 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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Graph Anomaly Detection jobs near you
Infographic showing various Graph Anomaly Detection job openings in the United States as of September 2026, with employment types broken down into 2% Internship, 1% As Needed, 83% Full Time, 12% Part Time, and 2% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $109,451 per year, or $52.6 per hour.

Data Scientist - Fraud Detection

Mountain View, CA • On-site

DataVisor
Software Development • 1 - 10 employees
Learn more about DataVisorexternal

Full-time

Medical, PTO

Posted 18 days ago

Job description

About DataVisor:
DataVisor is the world's leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's fraud and anti-money laundering (AML) solutions scale infinitely and enable organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine, and investigation tools work together to provide significant performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering total cost of ownership, compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.
Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results-driven. Come join us!
Position Overview:
We are looking for a motivated Entry-Level Data Scientist to join our Fraud Detection team. In this role, you will leverage your machine learning and data analysis skills to identify fraudulent activities, build predictive models, and uncover hidden patterns in large datasets. You will work closely with cross-functional teams to develop scalable solutions that enhance our fraud detection capabilities. This is a great opportunity to grow your skills in a fast-paced, data-driven environment while making a real impact in the fight against fraud.
Key Responsibilities:
  • Develop and deploy machine learning models for fraud detection and risk assessment.
  • Perform exploratory data analysis (EDA) to identify trends, anomalies, and patterns in transactional data.
  • Clean, preprocess, and analyze large datasets using Python and popular data science libraries (pandas, NumPy, scikit-learn, etc.).
  • Collaborate with engineering and business teams to integrate ML models into production systems.
  • Continuously monitor model performance and refine algorithms to improve accuracy.
  • Stay updated with the latest advancements in fraud detection techniques and ML/AI technologies.

Requirements
  • Master's degree in Computer Science, Data Science, Statistics, or a related quantitative field. Ph.D. degree is a plus.
  • Strong programming skills in Python and familiarity with data science libraries (NumPy, Pandas, scikit-learn, TensorFlow/PyTorch is a plus).
  • Solid understanding of machine learning algorithms (supervised/unsupervised learning, anomaly detection, classification, etc.).
  • Experience with SQL and data manipulation/analysis in large datasets.
  • Strong problem-solving skills and patience for deep-dive data exploration.
  • Prior internship or project experience in fraud modeling, risk analysis, or related fields is a plus.
  • Excellent communication skills and ability to work in a collaborative environment.

Nice to have
  • Familiarity with big data tools (Spark, Hadoop, Dask).
  • Knowledge of graph-based fraud detection techniques.
  • Experience with cloud platforms (AWS, GCP, Azure).

Benefits
PTO, Stock Option, Health Benefits

Apply