ZipRecruiter

Log In

1

Graph Anomaly Detection Jobs in New York (NOW HIRING)

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 ...

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 ...

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 ...

Appgate

Applied Scientist

Manhattan, NY · On-site

LLMs and graph reasoning to understand, simulate, and optimize security policies. * Threat Anomaly Detection & Breach Prediction: Deep models for early detection using behavioral, contextual, and ...

Cisco

Applied AI Scientist

Stamford, CT

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

Clera

Senior ML/AI Engineer

New York, NY · Remote

$170K - $230K/yr

... analysis, and anomaly detection at enterprise scale. * Develop and iterate on an agentic AI ... Hands-on experience with graph databases. * Strong SQL skills for data querying and manipulation.

Clera

Senior ML/AI Engineer

New York, NY · On-site

$170K - $230K/yr

... analysis, and anomaly detection at enterprise scale. * Develop and iterate on an agentic AI ... Hands-on experience with graph databases. * Strong SQL skills for data querying and manipulation.

Clera

Senior ML/AI Engineer

Manhattan, NY · On-site

$170 - $230/hr

... anomaly detection at enterprise scale * Develop and iterate on the platform's agentic AI ... Experience with graph databases * Proficiency with SQL Nice to Have * Experience working in startup ...

next page

Showing results 1-20

All JobsGraph Anomaly Detection JobsGraph Anomaly Detection Jobs in New York

Graph Anomaly Detection information

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.

What are popular job titles related to Graph Anomaly Detection jobs in New York?

For Graph Anomaly Detection jobs in New York, the most frequently searched job titles are:

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

The top searched job categories for Graph Anomaly Detection jobs in New York are:

What cities in New York are hiring for Graph Anomaly Detection jobs?

Cities in New York with the most Graph Anomaly Detection job openings:

Graph Anomaly Detection jobs near you

Principal Machine Learning Engineer

Appgate

Manhattan, NY • On-site

$220K - $265K/yr

Full-time

Re-posted 18 days ago

Job description

About the Role
We are seeking an exceptional Principal Machine Learning Engineer to lead the design and development of the next generation of our AI-driven fraud detection platform.
You will architect large-scale ML systems that detect and prevent fraud in real time combining deep machine learning expertise with scalable engineering and domain knowledge in financial systems.
This is a hands-on technical leadership role, shaping our fraud prevention roadmap and ensuring the platform evolves to meet emerging threat patterns through automation, data intelligence, and generative AI-enhanced detection models.
Responsibilities
  • Architect and build scalable ML systems for fraud detection, anomaly detection, and behavioral analysis.
  • Develop and maintain end-to-end ML pipelines: data ingestion, feature engineering, model training, deployment, and monitoring.
  • Leverage modern AI techniques, including generative AI, to improve fraud pattern discovery and model robustness.
  • Design and implement real-time decision systems, integrating with transaction or behavioral data streams.
  • Collaborate closely with engineering, security, and risk teams to define data strategy and labeling frameworks.
  • Lead experimentation on model explainability, drift detection, and adversarial robustness for fraud prevention use cases.
  • Promote engineering excellence - automation, CI/CD, reproducibility, observability, and model governance.
  • Mentor and guide ML and software engineers, fostering best practices and innovation.
Minimum Qualifications
  • 5+ years of experience building ML or AI systems in production; at least 2+ in fraud, risk, or anomaly detection domains.
  • Proven track record designing and maintaining ML pipelines at scale.
  • Expertise in Python, ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn), and CI/CD (GitHub Actions, Jenkins, or similar).
  • Strong understanding of supervised / unsupervised learning, anomaly detection, and statistical modeling.
  • Experience with big data and distributed systems (e.g., Spark, Kafka, Flink, or similar).
  • Familiarity with cloud platforms (AWS, GCP, or Azure) and containerized deployments (Docker, Kubernetes).
  • Strong collaboration, communication, and cross-team leadership skills.
Preferred Qualifications
  • Prior experience with fraud or financial crime detection, identity verification, or risk scoring systems.
  • Domain expertise in banking, payments, or transaction monitoring
  • Experience fine-tuning or adapting generative AI / large language models for pattern generation or synthetic data augmentation.
  • Familiarity with streaming analytics, graph ML, or time-series anomaly detection.
  • Knowledge of model governance, bias mitigation, and regulatory compliance in fraud contexts.
  • Contributions to fraud detection research, open-source, or AI publications.
What Success Looks Like
  • Real-time AI-driven fraud prevention models with measurable reduction in false positives and detection latency.
  • Scalable, automated ML pipelines enable faster experimentation and deployment.
  • Cross-functional collaboration delivering tangible business impact in fraud loss reduction.
  • A culture of ML excellence, experimentation, and continuous learning across the team.

Location: New York City
Department: AI / Fraud Prevention Engineering
Experience: 5+ years (Staff) or 8+ years (Principal) in ML or fraud detection systems
Compensation: 220-265k + bonus
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights notice from the Department of Labor.

Apply