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Fraud Detection Machine Learning Jobs in Queens, 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.

1Kosmos

AI / Machine Learning Engineer

Iselin, NJ · On-site

Apply fraud detection methods to identify tampering, forgeries, or replay attacks in visual data. * Integrate ML models into production-grade APIs and mobile SDKs. * Monitor, evaluate, and ...

Hyperproof

AI / Machine Learning Engineer

Woodbridge, NJ · On-site

$100 - $130/hr

Apply fraud detection methods to identify tampering, forgeries, or replay attacks in visual data. * Integrate ML models into production-grade APIs and mobile SDKs. * Monitor, evaluate, and ...

1Kosmos

AI / Machine Learning Engineer

Iselin, NJ · On-site

Apply fraud detection methods to identify tampering, forgeries, or replay attacks in visual data. * Integrate ML models into production-grade APIs and mobile SDKs. * Monitor, evaluate, and ...

1Kosmos

AI / Machine Learning Engineer

Iselin, NJ · Hybrid

Apply fraud detection methods to identify tampering, forgeries, or replay attacks in visual data. * Integrate ML models into production-grade APIs and mobile SDKs. * Monitor, evaluate, and ...

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

Paterson, NJ · 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

White Plains, 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

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:

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All JobsFraud Detection Machine Learning JobsFraud Detection Machine Learning Jobs in Queens, NY

Fraud Detection Machine Learning information

See Queens, NY salary details

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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 Queens, NY is $18.84, according to ZipRecruiter salary data. Most workers in this role earn between $15.53 and $20.05 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 are popular job titles related to Fraud Detection Machine Learning jobs in Queens, NY? For Fraud Detection Machine Learning jobs in Queens, NY, the most frequently searched job titles are:
What job categories do people searching Fraud Detection Machine Learning jobs in Queens, NY look for? The top searched job categories for Fraud Detection Machine Learning jobs in Queens, NY are:
What cities near Queens, NY are hiring for Fraud Detection Machine Learning jobs? Cities near Queens, NY with the most Fraud Detection Machine Learning job openings:
Fraud Detection Machine Learning jobs near you

AI / Machine Learning Engineer

Apogee Global RMS

Piscataway, NJ • On-site

Full-time

Posted 12 days ago

Job description

Apogee Global RMS is seeking an AI / Machine Learning Engineer to support our enterprise client in New Jersey. This role will focus on designing, building, and deploying advanced AI and computer vision solutions that drive innovation in identity, security, automation, and digital trust.
The ideal candidate will have strong experience developing production-grade machine learning models, working across the full ML lifecycle-from data preparation and model development to deployment and optimization across cloud and mobile environments.
Key Responsibilities:
  • Design and develop AI/ML models for computer vision, image classification, object detection, OCR, and feature extraction.
  • Build real-time image quality assessment, data processing, and intelligent capture solutions.
  • Develop and maintain data pipelines for data collection, labeling, cleaning, and augmentation.
  • Optimize ML models for cloud and mobile/on-device inference.
  • Implement fraud detection, anomaly detection, and security-focused AI capabilities.
  • Integrate ML models into production APIs and software platforms.
  • Monitor model performance and continuously improve accuracy and scalability.
  • Collaborate with engineering, product, and business teams to deliver AI-driven solutions.

Requirements
  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or related field (or equivalent experience).
  • 3+ years of experience building and deploying machine learning models in production environments.
  • Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or similar.
  • Experience with computer vision libraries (OpenCV) and OCR technologies.
  • Strong understanding of deep learning architectures for image and text recognition.
  • Experience with cloud platforms such as AWS, Azure, or GCP.
  • Strong problem-solving skills and ability to thrive in a fast-paced environment.
Preferred Qualifications:
  • Experience with model optimization and quantization for mobile deployment.
  • Knowledge of synthetic data generation and data augmentation techniques.
  • Background in fraud detection, anomaly detection, security, or identity technologies.
  • Familiarity with data privacy and compliance standards.
  • Experience contributing to open-source projects or AI research.
What You'll Do:
  • Build next-generation AI capabilities with real-world enterprise impact.
  • Work on innovative computer vision and machine learning challenges.
  • Partner with talented engineering teams to move AI solutions from research to production.
  • Drive improvements in accuracy, performance, and scalability.

Benefits
For any questions (OR) to apply, please contact us at careers@apogeeglobalrms.com.

Apply