1

Fraud Detection Machine Learning Jobs in Chicago, IL

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 ...

This job will validate and develop machine learning models and algorithms to solve complex problems ... Relevant Modeling experience in credit scoring, fraud detection, financial forecasting, or ...

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

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

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

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

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

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

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

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

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

This job will validate and develop machine learning models and algorithms to solve complex problems ... Relevant Modeling experience in credit scoring, fraud detection, financial forecasting, or ...

This job will validate and develop machine learning models and algorithms to solve complex problems ... Relevant Modeling experience in credit scoring, fraud detection, financial forecasting, or ...

Primary Skills • Insurance Claims Analytics • Risk Analysis • Fraud DetectionMachine Learning • Natural Language Processing (NLP) • Python • SQL • Spark • Operations Research (LP ...

Sr Machine Learning Engineer

Chicago, IL

$57.50 - $76/hr

Develop and optimize machine learning models for various applications. * Preprocess and analyze ... fraud detection, financial forecasting, or marketing analytics - gained through industry or ...

Sr Machine Learning Engineer

Chicago, IL · On-site

$57.50 - $76/hr

Develop and optimize machine learning models for various applications. * Preprocess and analyze ... fraud detection, financial forecasting, or marketing analytics - gained through industry or ...

next page

Showing results 1-20

Fraud Detection Machine Learning information

See Chicago, IL salary details

$11

$18

$27

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

As of Jun 16, 2026, the average hourly pay for fraud detection machine learning in Chicago, IL is $18.60, according to ZipRecruiter salary data. Most workers in this role earn between $15.34 and $19.81 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 Chicago, IL? For Fraud Detection Machine Learning jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Fraud Detection Machine Learning jobs in Chicago, IL look for? The top searched job categories for Fraud Detection Machine Learning jobs in Chicago, IL are:
What cities near Chicago, IL are hiring for Fraud Detection Machine Learning jobs? Cities near Chicago, IL with the most Fraud Detection Machine Learning job openings:
Infographic showing various Fraud Detection Machine Learning job openings in Chicago, IL as of June 2026, with employment types broken down into 100% Full Time. Highlights an 72% In-person, and 28% Hybrid job distribution, with an average salary of $38,680 per year, or $18.6 per hour.

Machine Learning Engineer

Kasmo Global

Chicago, IL

Other

Posted yesterday


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