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Anomaly Detection Jobs (NOW HIRING)

Appgate

Staff Machine Learning Engineer

Manhattan, NY · On-site

$180K - $220K/yr

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 ...

Coforge

Python Developer

Berkeley Heights, NJ · On-site

$52.50 - $72.25/hr

Job Title - Python Developer Skills - Python, Snowflake, Advanced SQL, CUSUM, Anomaly Detection, Statistics, FDR, Beta-Binomial, ODBC, Pandas, NumPy, ETL, Window Functions, VARIANT Columns, Data ...

DecisionPoint | Cortek

AI/ML Architect

$65.25 - $84/hr

This role develops predictive analytics solutions, anomaly detection models for IT and cybersecurity operations, automated content classification capabilities, personalization algorithms, and ...

Prophecy Technologies

Senior AIOps ML Engineer

Los Angeles, CA · On-site

$112K - $154K/yr

This role involves developing and deploying advanced machine learning models for AIOps, including streaming anomaly detection, root-cause analysis, and incident forecasting. Key responsibilities also ...

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Anomaly Detection information

What is anomaly detection?

An Anomaly Detection job involves identifying unusual patterns or deviations in data that do not conform to expected behavior. Professionals in this role use statistical methods, machine learning, and AI techniques to detect fraudulent activities, network intrusions, or system failures. They work in various industries such as finance, cybersecurity, healthcare, and manufacturing. Responsibilities may include data preprocessing, model training, and real-time anomaly detection to improve security and operational efficiency.

What does someone working in anomaly detection do?

Professionals in Anomaly Detection typically spend their days analyzing large datasets to identify unusual patterns or behaviors that could indicate errors, fraud, or other significant events. They build and maintain models using statistical techniques and machine learning algorithms, validate detected anomalies, and collaborate closely with data engineers, cybersecurity teams, or business analysts depending on the industry. Regular reporting of findings, tuning detection systems for accuracy, and staying updated with emerging methodologies are also important aspects of the job. The role often requires working both independently and as part of a multidisciplinary team to ensure timely and actionable insights are delivered.

What are the key skills and qualifications needed to thrive in anomaly detection?

To thrive in an Anomaly Detection role, you need a strong background in data analysis, statistics, and machine learning, often supported by a degree in computer science, mathematics, or a related field. Familiarity with programming languages like Python or R, and experience using data analysis tools such as TensorFlow, Scikit-learn, or specialized anomaly detection frameworks, are typically required. Strong problem-solving skills, attention to detail, and effective communication enhance your ability to interpret findings and share insights with cross-functional teams. These skills are essential for accurately identifying unusual patterns in data and contributing to an organization's data-driven decision-making processes.

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What are the most commonly searched types of Anomaly Detection jobs?

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What states have the most Anomaly Detection jobs?

States with the most job openings for Anomaly Detection jobs include:

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

The top searched job categories for Anomaly Detection jobs are:

Anomaly Detection jobs near you
Infographic showing various Anomaly Detection job openings in the United States as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Staff Machine Learning Engineer

Appgate

Manhattan, NY • On-site

$180K - $220K/yr

Full-time

Re-posted 10 days ago

Job description

About the Role
We are seeking an exceptional Staff 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: 180-220k + 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.

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