Machine Learning Engineer
San Jose, CA · On-site
Machine Learning Engineer Location: San Jose, CA/Chicago, IL Duration: 18 months contract with a ... fraud detection • Architect large-scale big-data infrastructure to enable use of cutting-edge ...
Machine Learning Engineer
San Jose, CA · On-site
Machine Learning Engineer Location: San Jose, CA/Chicago, IL Duration: 18 months contract with a ... fraud detection • Architect large-scale big-data infrastructure to enable use of cutting-edge ...
Machine Learning Engineer Location: San Jose, CA/Chicago, IL Duration: 18 months contract with a ... fraud detection • Architect large-scale big-data infrastructure to enable use of cutting-edge ...
Machine Learning Engineer Location: San Jose, CA/Chicago, IL Duration: 18 months contract with a ... fraud detection • Architect large-scale big-data infrastructure to enable use of cutting-edge ...
Fraud Investigations Analyst
Mclean, VA · On-site
Familiarity with machine learning-driven fraud detection systems or AI-assisted analysis
Fraud Investigations Analyst
Mclean, VA · On-site
Familiarity with machine learning-driven fraud detection systems or AI-assisted analysis
Use fraud detection tools, machine learning outputs, and risk-scoring systems to drive high-quality investigations * Independently own investigations from detection through resolution, including ...
Use fraud detection tools, machine learning outputs, and risk-scoring systems to drive high-quality investigations * Independently own investigations from detection through resolution, including ...
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.
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.
Director, Machine Learning
Austin, TX · On-site
Leverage generative AI techniques to enhance fraud detection models, creating synthetic data for robust testing and model training. Incorporate generative AI into the machine learning strategy to ...
Director, Machine Learning
Austin, TX · On-site
Leverage generative AI techniques to enhance fraud detection models, creating synthetic data for robust testing and model training. Incorporate generative AI into the machine learning strategy to ...
Director, Machine Learning
Austin, TX · Hybrid
Leverage generative AI techniques to enhance fraud detection models, creating synthetic data for robust testing and model training. Incorporate generative AI into the machine learning strategy to ...
Director, Machine Learning
Austin, TX · Hybrid
Leverage generative AI techniques to enhance fraud detection models, creating synthetic data for robust testing and model training. Incorporate generative AI into the machine learning strategy to ...
Fraud Investigations Analyst
Mclean, VA · On-site
$80K - $93K/yr
Familiarity with machine learning-driven fraud detection systems or AI-assisted analysis The annual base salary listed does not include a company bonus, incentive for sales roles, equity and benefits ...
Fraud Investigations Analyst
Mclean, VA · On-site
$80K - $93K/yr
Familiarity with machine learning-driven fraud detection systems or AI-assisted analysis The annual base salary listed does not include a company bonus, incentive for sales roles, equity and benefits ...
Fraud Investigations Analyst
$80K - $93K/yr
Familiarity with machine learning-driven fraud detection systems or AI-assisted analysis The annual base salary listed does not include a company bonus, incentive for sales roles, equity and benefits ...
Quick apply
Fraud Investigations Analyst
$80K - $93K/yr
Familiarity with machine learning-driven fraud detection systems or AI-assisted analysis The annual base salary listed does not include a company bonus, incentive for sales roles, equity and benefits ...
Manager, Machine Learning
$180K - $210K/yr
You will lead a team of ML data scientists on the Fraud and ML team, owning the development and quality of Extend's machine learning models across fraud detection, risk assessment, and identity ...
Manager, Machine Learning
$180K - $210K/yr
You will lead a team of ML data scientists on the Fraud and ML team, owning the development and quality of Extend's machine learning models across fraud detection, risk assessment, and identity ...
Develop and implement fraud detection models using advanced analytics and machine learning techniques. * Monitor real-time transaction data to identify and flag potentially fraudulent activities.
Develop and implement fraud detection models using advanced analytics and machine learning techniques. * Monitor real-time transaction data to identify and flag potentially fraudulent activities.
Develop and implement fraud detection models using advanced analytics and machine learning techniques. * Monitor real-time transaction data to identify and flag potentially fraudulent activities.
Develop and implement fraud detection models using advanced analytics and machine learning techniques. * Monitor real-time transaction data to identify and flag potentially fraudulent activities.
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.
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.
Develop and implement fraud detection models using advanced analytics and machine learning techniques. * Monitor real-time transaction data to identify and flag potentially fraudulent activities.
Develop and implement fraud detection models using advanced analytics and machine learning techniques. * Monitor real-time transaction data to identify and flag potentially fraudulent activities.
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.
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.
Develop and implement fraud detection models using advanced analytics and machine learning techniques. * Monitor real-time transaction data to identify and flag potentially fraudulent activities.
Develop and implement fraud detection models using advanced analytics and machine learning techniques. * Monitor real-time transaction data to identify and flag potentially fraudulent activities.
Our machine learning systems power real-time advertising use cases including bidding, ranking, pacing, and fraud detection. These models operate in extremely latency-sensitive environments, serving ...
Our machine learning systems power real-time advertising use cases including bidding, ranking, pacing, and fraud detection. These models operate in extremely latency-sensitive environments, serving ...
Our machine learning systems power real-time advertising use cases including bidding, ranking, pacing, and fraud detection. These models operate in extremely latency-sensitive environments, serving ...
Our machine learning systems power real-time advertising use cases including bidding, ranking, pacing, and fraud detection. These models operate in extremely latency-sensitive environments, serving ...
Cyber Fraud Investigations Analyst
Chicago, IL · Remote
$40 - $49.14/hr
... machine learning models to create a more streamlined end-to-end process in mitigating fraud ... Collaborate with Data Analytics and Data Engineering to detect, scale, and monitor fraud/abuse ...
Quick apply
Cyber Fraud Investigations Analyst
Chicago, IL · Remote
$40 - $49.14/hr
... machine learning models to create a more streamlined end-to-end process in mitigating fraud ... Collaborate with Data Analytics and Data Engineering to detect, scale, and monitor fraud/abuse ...
Our machine learning systems power real-time advertising use cases including bidding, ranking, pacing, and fraud detection. These models operate in extremely latency-sensitive environments, serving ...
Our machine learning systems power real-time advertising use cases including bidding, ranking, pacing, and fraud detection. These models operate in extremely latency-sensitive environments, serving ...
Fraud Detection Machine Learning information
See salary details
$10.82 - $12.28
4% of jobs
$12.28 - $13.75
9% of jobs
$14.87 is the 25th percentile. Wages below this are outliers.
$13.75 - $15.21
15% of jobs
$15.21 - $16.67
20% of jobs
The median wage is $16.80 / hr.
$16.67 - $18.14
19% of jobs
$18.80 is the 75th percentile. Wages above this are outliers.
$18.14 - $19.60
17% of jobs
$19.60 - $21.07
7% of jobs
$21.07 - $22.53
4% of jobs
$22.53 - $23.99
2% of jobs
$23.99 - $25.46
1% of jobs
$25.46 - $26.92
1% of jobs
$10
$18
$26
How much do fraud detection machine learning jobs pay per hour?
As of Jun 13, 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?
| Aspect | Fraud Detection Machine Learning | Fraud Analyst |
|---|---|---|
| Credentials | Data science, machine learning certifications, programming skills | Finance, criminal justice degrees, analytical skills |
| Work Environment | Data-driven, tech-focused, often in financial or e-commerce sectors | Investigative, report-focused, in financial institutions or insurance companies |
| Employer & Industry | Tech companies, banks, e-commerce platforms | Financial 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.
More about Fraud Detection Machine Learning jobs
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:
What job categories do people searching Fraud Detection Machine Learning jobs look for? The top searched job categories for Fraud Detection Machine Learning jobs are:
Other
Posted 7 days ago
Job description
Machine Learning Engineer
Location: San Jose, CA/Chicago, IL
Duration: 18 months contract with a possible extension
What You'll Do• Redesign and optimize PayPal's MLOps and decision platform for fraud detection
• Architect large-scale big-data infrastructure to enable use of cutting-edge machine learning models for real-time fraud prevention.
• Collaborate with data scientists and platform engineers to automate workflow
• Provide solutions that ensure compliance, security, and maintainability across the fraud detection ecosystem.
• Work with high-dimensional datasets and leverage tools like Python, PySpark, and Big Query to develop robust workflows for fraud signal detection.
• Standardize rules and decision processes while enabling dynamic rule updates and analytics within the fraud detection platform.
• Collaborate across multidisciplinary teams in engineering, product development, and data science to scale solutions globally.
• Tasks will be distributed via our Jira board/sprint planning/grooming cycle.
• Team will have a regular standup on each task (at least twice a week but open for daily if needed or any blockers).
• During the onboarding, it would require more interactions with Engg/Product/US Risk core teams but once onboarded, it would be 50/50.
• Team work mostly within Jira board from tasks assignments and tracking.
• Code would be in our centralized GitHub repo.
• Updates/documentation would be either in wiki page or our SharePoint/share drive.
You would get chance to design and implement scalable solutions to optimize fraud detection systems, spanning model development, feature engineering, and rule-based systems. You will collaborate closely with cross-functional teams, including data scientists, engineers, and product managers, to ensure our platform sets a new global standard for efficacy and innovation. You will address critical business challenges, develop advanced automation frameworks, and integrate cutting-edge machine learning techniques to enhance decision-making capabilities. By joining us, you will not only contribute to PayPal's fraud detection efforts but leave a lasting impact on the financial security of millions of users around the globe.
Top Skills:- Big Query, Python, SQL
- Understand the production systems architect and offline data overview
- Machine Learning experience