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Fraud Detection Machine Learning Jobs in Wantagh, NY

TigerGraph

Account Executive

New York, NY · On-site

$150K - $160K/yr

Its proven technology supports fraud detection, customer 360, MDM, IoT, AI, and machine learning. Fortune 500 organizations and the most innovative mid-size and startup companies choose TigerGraph to ...

Mercor

Engineering Manager, Fraud & Compliance

New York, NY · On-site

$250K - $400K/yr

Fraud detection and platform integrity - identity verification, abuse prevention, risk scoring, and ... machine learning, AI systems, decision engines, or risk-scoring platforms Benefits * Generous ...

Taptap Send

Head of Fraud

Manhattan, NY · On-site

$180 - $280/hr

Leverage data analytics to enhance fraud detection, transaction monitoring, and identity ... Hands‑on experience with analytics, machine learning models, and automation tools. * Strong ...

Taptap Send

Head of Fraud

New York, NY · On-site

Leverage data analytics to enhance fraud detection, transaction monitoring, and identity ... Hands-on experience with analytics, machine learning models, and automation tools. * Strong ...

ExaCare AI

Machine Learning Engineer

Manhattan, NY · On-site

$110 - $150/hr

We are seeking a highly adaptable, creative, and well‑rounded Machine Learning Engineer to join ... Implement and maintain robust monitoring systems to track model performance, detect drift, and ...

J.P. Morgan

Fraud AI/ML Platform Product Director

New York, NY · On-site

$254K - $266K/yr

Strong technical fluency across applied machine learning, data systems, and production constraints ... Experience productizing graph features/embeddings or graph ML for fraud ring detection, network ...

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

Fraud Detection Machine Learning information

See Wantagh, 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 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

Senior Product Manager - Document Verification

Socure

New York, NY • On-site

$170K - $205K/yr

Full-time

Re-posted 22 days ago

Job description

Why Socure?
Socure is building the identity trust infrastructure for the digital economy - verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.
We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won't be your place. If you want to help build the future of identity with a team that holds a high bar for itself - keep reading.
About the Role
We are looking for a Senior Product Manager to own critical components of Socure's Document Verification (DocV) platform, with a focus on the forensic engine, decisioning logic, and computer vision models.
This role sits at the intersection of machine learning, fraud detection, and product decisioning, and is responsible for driving the systems that translate signals into outcomes. You will work closely with Data Science and Engineering to improve detection of fraud vectors such as injection attacks, deepfakes, and document manipulation, while also shaping how those signals are operationalized into scalable and configurable decisioning frameworks.
This is a highly technical and impact-driven role requiring strong product judgment, deep curiosity about fraud patterns, and the ability to translate complex model behavior into clear product logic and customer-facing outcomes.
What You'll Do
Forensic Engine & Detection Strategy
  • Own the roadmap and execution for DocV's forensic engine, including detection of document fraud, injection attacks, and AI-generated content.
  • Support efforts to scale DocV adoption globally, including in the public sector, financial services, and emerging markets.
  • Partner with Data Science to define, evaluate, and improve model performance across key fraud vectors.
  • Identify gaps in detection coverage and drive new signal development across image, video, and device layers.

Decisioning & Risk Logic
  • Design and evolve decisioning frameworks that translate model outputs into actionable outcomes.
  • Build scalable, configurable logic that supports diverse customer risk profiles and use cases.
  • Balance fraud detection performance with user experience and conversion impact.

Customer-Centric Product Development
  • Work closely with high-value customers to understand fraud patterns, edge cases, and operational needs.
  • Translate customer feedback into product improvements and prioritization decisions.
  • Support complex customer implementations and act as a subject matter expert in DocV decisioning.

Cross-Functional Leadership
  • Collaborate with Engineering and Data Science to translate product requirements into technical execution.
  • Partner with the Fraud Investigation team, Customer Success, and Sales to align on product behavior and outcomes.
  • Drive alignment on tradeoffs between detection accuracy, false positives, and business impact.

Data & Performance Analysis
  • Use SQL and analytics tools to evaluate model performance, decisioning outcomes, and conversion impact.
  • Define and track key metrics related to fraud detection, model precision/recall, and user experience.
  • Conduct deep dives into fraud patterns and emerging attack vectors.

Go-To-Market & Enablement
  • Support product launches and enhancements with clear positioning and documentation.
  • Enable internal teams and customers to understand and effectively use decisioning capabilities.

What You Bring
  • Experience: 3-5 years in product management, preferably in identity verification, fraud prevention, or other ML-driven products.
  • Technical Expertise: Strong understanding of APIs, SQL queries, databases, and product architecture.
  • Machine Learning Familiarity: Experience working closely with ML models, including understanding model outputs, evaluation metrics, and tradeoffs.
  • Computer Vision (Preferred): Exposure to image processing, OCR, or document verification systems is a strong plus.
  • Fraud & Identity Domain Knowledge: Familiarity with fraud detection techniques, identity verification flows, or risk-based decisioning systems.
  • Analytical Skills: Comfortable working with data, writing queries, and deriving insights to inform product decisions.
  • Product Judgment: Ability to balance technical complexity, customer needs, and business impact in decision-making.
  • Customer Focus: Experience working directly with customers, especially in complex or high-stakes environments.
  • Communication: Strong ability to explain complex technical concepts clearly to both technical and non-technical audiences.
  • Collaboration: Proven ability to work cross-functionally with Engineering, Data Science, and go-to-market teams.

Note: We cannot provide Sponsorship at this time.
You must be located in one of our talent hubs: SF, NY, Seattle, Miami, or DC.
Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you need an accommodation during any stage of the application or hiring process-including interview or onboarding support-please reach out to your Socure recruiting partner directly.
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