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Fraud Detection Machine Learning Jobs in Wantagh, NY

Appgate

Staff Machine Learning Engineer

Manhattan, NY · On-site

$180K - $220K/yr

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.

Appgate

Principal Machine Learning Engineer

Manhattan, NY · On-site

$220K - $265K/yr

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.

Fin

Risk Analyst

New York, NY · Remote

$100K - $175K/yr

Partner with the engineering team to design and implement fraud detection systems, leveraging machine learning and predictive analytics. * Ensure alignment with regulatory requirements, including AML ...

Fin

Risk Analyst

New York, NY · On-site

$100K - $175K/yr

Partner with the engineering team to design and implement fraud detection systems, leveraging machine learning and predictive analytics. * Ensure alignment with regulatory requirements, including AML ...

Fin

Risk Analyst

New York, NY · On-site +1

$100K - $175K/yr

Partner with the engineering team to design and implement fraud detection systems, leveraging machine learning and predictive analytics. * Ensure alignment with regulatory requirements, including AML ...

Varsity Tutors

Machine Learning Tutor

Glen Cove, NY · Remote

$18 - $40/hr

Emphasizes practical model development workflow and connects machine learning to recommendation systems, fraud detection, and predictive analytics. * Curriculum Awareness & Adaptive Instruction:

Varsity Tutors

Machine Learning Tutor

Hempstead, NY · Remote

$18 - $40/hr

Emphasizes practical model development workflow and connects machine learning to recommendation systems, fraud detection, and predictive analytics. * Curriculum Awareness & Adaptive Instruction:

Varsity Tutors

Machine Learning Tutor

Mount Vernon, NY · Remote

$18 - $40/hr

Emphasizes practical model development workflow and connects machine learning to recommendation systems, fraud detection, and predictive analytics. * Curriculum Awareness & Adaptive Instruction:

Varsity Tutors

Machine Learning Tutor

New Rochelle, NY · Remote

$18 - $40/hr

Emphasizes practical model development workflow and connects machine learning to recommendation systems, fraud detection, and predictive analytics. * Curriculum Awareness & Adaptive Instruction:

Alloy

Senior Data Scientist

New York, NY · On-site

Our immediate focus is on fraud detection, where we believe machine learning can simplify and accelerate decision-making in ways traditional rule-based systems can't. Managing rules and policies to ...

Alloy

Senior Data Scientist

New York, NY · Hybrid

Our immediate focus is on fraud detection, where we believe machine learning can simplify and accelerate decision-making in ways traditional rule-based systems can't. Managing rules and policies to ...

Whatnot

Senior Software Engineer, Fraud

Manhattan, NY · On-site

$180 - $240/hr

Lead the full architecture of fraud detection, prevention, and intervention systems -- spanning machine learning, backend, and client-side components. * Build intelligent user graphs to model ...

New

Warnerbros

Senior Machine Learning Engineer

New York, NY · On-site

$114K - $157K/yr

About the Role We're seeking a Senior Machine Learning Engineer to develop and deploy machine learning solutions for consumer growth, including fraud detection, pricing optimization, and user ...

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Fraud Detection Machine Learning information

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How much do fraud detection machine learning jobs pay per hour?

As of Aug 12, 2026, the average hourly pay for fraud detection machine learning in Wantagh, NY is $18.35, according to ZipRecruiter salary data. Most workers in this role earn between $15.14 and $19.57 per hour, depending on experience, location, and employer.

What are some common challenges faced by professionals working in fraud detection machine learning, and how can they be addressed?

Professionals in Fraud Detection Machine Learning often face challenges such as dealing with highly imbalanced datasets, rapidly evolving fraud patterns, and the need for real-time detection. Managing data imbalance requires careful selection of evaluation metrics and specialized algorithms. Staying ahead of new fraud tactics involves continuous model retraining and close collaboration with domain experts. Additionally, integrating machine learning solutions with existing systems often requires cross-functional teamwork with IT, security, and compliance teams.

What is fraud detection using machine learning?

Fraud detection using machine learning involves leveraging algorithms and data analysis techniques to identify suspicious or fraudulent activities in various domains, such as banking, e-commerce, or insurance. These systems analyze large volumes of transaction data to detect patterns or anomalies that may indicate fraud. Machine learning models can adapt over time, improving their accuracy as they are exposed to more data. This approach helps organizations automate and enhance their ability to prevent, detect, and respond to fraudulent behavior efficiently.

What is the difference between Fraud Detection Machine Learning vs Fraud Analyst?

AspectFraud Detection Machine LearningFraud Analyst
CredentialsData science, machine learning certifications, programming skillsFinance, criminal justice degrees, analytical skills
Work EnvironmentData-driven, tech-focused, often in financial or e-commerce sectorsInvestigative, report-focused, in financial institutions or insurance companies
Employer & IndustryTech companies, banks, e-commerce platformsFinancial institutions, insurance firms, retail

Fraud Detection Machine Learning involves developing algorithms to identify fraudulent activities automatically, relying heavily on data analysis and programming. Fraud Analysts manually investigate suspicious cases and interpret data insights. While both roles aim to prevent fraud, Machine Learning specialists focus on building models, whereas Fraud Analysts focus on case investigation and decision-making.

What are the key skills and qualifications needed to thrive as a fraud detection machine learning specialist, and why are they important?

To thrive as a Fraud Detection Machine Learning Specialist, you need strong expertise in machine learning, statistical analysis, and programming languages like Python or R, typically supported by a degree in computer science, data science, or a related field. Familiarity with tools such as TensorFlow, Scikit-learn, SQL databases, and experience with big data platforms or cloud services is highly valuable. Critical thinking, attention to detail, and effective communication are crucial soft skills for identifying complex fraud patterns and collaborating with interdisciplinary teams. These competencies are vital for developing accurate models that protect organizations from financial losses and maintain trust with customers.
What cities near Wantagh, NY are hiring for Fraud Detection Machine Learning jobs? Cities near Wantagh, NY with the most Fraud Detection Machine Learning job openings:
Fraud Detection Machine Learning jobs near you

Staff Machine Learning Engineer

Appgate

Manhattan, NY • On-site

$180K - $220K/yr

Full-time

Re-posted 13 hours 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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