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Fraud Detection Machine Learning Jobs (NOW HIRING)

SentiLink

Head of Machine Learning - Application Fraud

$233K/yr

As the Head of Machine Learning for Application Fraud, you will lead a team of data scientists in developing fraud detection models and enhancing SentiLink's product offerings while being a technical ...

Coinflow

Machine Learning Lead

Chicago, IL · On-site

$175K - $235K/yr

Strengthen Coinflow's fraud detection and risk decisioning capabilities - feature engineering ... in machine learning, applied data science, or production ML roles * Demonstrated experience ...

Socure

Staff Data Scientist - Fraud & Risk

New York, NY · On-site

$180K - $210K/yr

... fraud detection. * Present findings and recommendations to technical and executive stakeholders with clarity and influence. * Stay current with advancements in AI and machine learning, applying ...

TigerGraph

Stay in touch

Redwood City, CA

$140K - $168K/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 ...

TigerGraph

Stay in touch

Redwood City, CA

$140K - $168K/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 ...

TigerGraph

Stay in touch

Redwood City, CA · On-site

$140K - $168K/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 ...

Photon

Machine Learning Engineer - NJ

Addison, TX

$54 - $71.50/hr

Apply expertise in data mining and machine learning techniques, including forecasting, prediction, segmentation, recommendation, and fraud detection. Data Engineering and Preparation: * Extend and ...

centralins

Anti-Fraud Manager

Dublin, OH · On-site +1

... and machine learning. * Reviews and evaluates all available information to assess the ... fraud detection, prevention, and suspect claim handling measures. * Represents the Company at ...

centralins

Anti-Fraud Manager

Waltham, MA · On-site +1

... and machine learning. * Reviews and evaluates all available information to assess the ... fraud detection, prevention, and suspect claim handling measures. * Represents the Company at ...

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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 Jul 22, 2026, the average hourly pay for fraud detection machine learning in the United States is $18.05, according to ZipRecruiter salary data. Most workers in this role earn between $14.90 and $19.23 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.
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What cities are hiring for Fraud Detection Machine Learning jobs? Cities with the most Fraud Detection Machine Learning job openings:
What states have the most Fraud Detection Machine Learning jobs? States with the most job openings for Fraud Detection Machine Learning jobs include:
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Fraud Detection Machine Learning jobs near you
Infographic showing various Fraud Detection Machine Learning job openings in the United States as of July 2026, with employment types broken down into 94% Full Time, 4% Part Time, and 2% Contract. Highlights an 92% Physical, 2% Hybrid, and 6% Remote job distribution, with an average salary of $37,548 per year, or $18.1 per hour.
Head of Machine Learning - Application Fraud

Head of Machine Learning - Application Fraud

SentiLink

Remote

$233K/yr

Full-time

Posted 20 days ago

Job description

Job Summary:
SentiLink is a company providing innovative identity and risk solutions, focused on transforming identity verification in the United States. As the Head of Machine Learning for Application Fraud, you will lead a team of data scientists in developing fraud detection models and enhancing SentiLink's product offerings while being a technical mentor and owner of your domain.
Responsibilities:
• Directly manage a team of highly skilled data scientists. Start from 2-3 and grow to 5-6.
• Act as a technical mentor that can dive deep and provide detailed direction.
• Lead planning, resourcing and communications with senior leadership, product and engineering teams.
• Develop strong business intuition and guide your team to deliver performant DS solutions on aggressive timelines.
• Develop and maintain SentiLink’s fraud detection models through the full model development lifespan: from data acquisition decisions through featurization, focusing labeling resources, model training, experimentation, productionalization, and monitoring.
• Research new types of fraud and develop new SentiLink products around identity verification.
• Achieve success by researching / developing through iteration, integration of new data sources and inventive feature engineering.
• Write production-ready code that can be relied on for real-time decision making by our partners.
• Design, perform, and present analyses that will inform data acquisition, product development, risk operations priorities, marketing, and sales efforts.
Qualifications:
Required:
• 10+ years relevant work experience & relevant Masters or 7+ years & relevant PhD.
• 2-5 years experience directly managing a team of data scientists, ideally in a startup environment.
• 3+ years of startup experience.
• Excellent communicator and team player.
• Proven track record of solving complex / high profile business problems with DS / ML solutions.
• Experience in communicating outcomes / progress to senior management / stakeholders.
• Very strong in “end to end” DS development: Planning, fleshing out success criteria / metrics, getting buy-in, developing the solution, delivering the solution (prod / deck / strategy doc / etc).
• Strong practical ML / Stats knowledge, i.e. can easily employ the suite of standard ML / stats tools to quickly scope out solutions, and double down where needed.
• Interest in developing deep domain expertise for product-focused work: a background in fraud is not required, but willingness to learn is.
• Experience writing production code and tests.
• Detail oriented and thoughtful—someone we can rely on to make business-changing decisions.
• Thrive in a fast paced environment characterized by the need to solve extremely varied, high impact, open ended problems.
• Candidates must be legally authorized to work in the United States and must live in the United States.
Preferred:
• Experience with SOTA ML solutions is a plus.
• Bonus for familiarity with: identity solutions, fintech, or adjacent industries.
Company:
SentiLink is an identity verification technology company that helps in detecting and blocking synthetic identities. Founded in 2017, the company is headquartered in San Francisco, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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