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Fraud Detection Machine Learning Jobs (NOW HIRING)

Photon

Machine Learning Engineer - NJ

Dallas, TX

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

ID.me

Senior Fraud Investigations Analyst

Mclean, VA · On-site

Use fraud detection tools, machine learning outputs, and risk-scoring systems to drive high-quality investigations * Independently own investigations from detection through resolution, including ...

PayPal, Inc.

Sr Machine Learning Engineer

San Jose, CA · On-site

$143K - $189K/yr

Experiment with innovative models and new approaches to enhance fraud detection capabilities ... Special Skill Requirements: 1. Machine learning frameworks and libraries such as TensorFlow ...

PayPal

Sr Machine Learning Engineer

San Jose, CA

$143K - $189K/yr

Experiment with innovative models and new approaches to enhance fraud detection capabilities ... Special Skill Requirements: 1. Machine learning frameworks and libraries such as TensorFlow ...

PayPal

Sr Machine Learning Engineer

San Jose, CA

$143K - $189K/yr

Experiment with innovative models and new approaches to enhance fraud detection capabilities ... Special Skill Requirements: 1. Machine learning frameworks and libraries such as TensorFlow ...

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 ...

Sift Science, Inc

Machine Learning Engineer

$140K - $190K/yr

As a Machine Learning Engineer at Sift, you will bridge the gap between data science and large ... Experience explicitly in the fraud detection, risk mitigation, or cyber-security domains. * Deep ...

Elite WorkForce Inc

Data Scientist at GA

Atlanta, GA · On-site

Develop and deploy machine learning models for fraud detection. * Collaborate with engineering and compliance teams for real-time solutions. * Design KPIs to assess model performance and improve ...

Grubhub

Analyst, Fraud Strategy

New York, NY · On-site

$96K - $102K/yr

Familiarity with advanced fraud detection systems, real-time monitoring frameworks, or machine learning approaches. * Strong track record of effectively presenting insights to senior management and ...

Grubhub

Analyst, Fraud Strategy

Chicago, IL · On-site

$96K - $102K/yr

Familiarity with advanced fraud detection systems, real-time monitoring frameworks, or machine learning approaches. * Strong track record of effectively presenting insights to senior management and ...

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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 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?

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.
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:
Fraud Detection Machine Learning jobs near you
Infographic showing various Fraud Detection Machine Learning job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 67% In-person, and 33% Hybrid job distribution, with an average salary of $37,548 per year, or $18.1 per hour.
Machine Learning Engineer - NJ

Machine Learning Engineer - NJ

Photon

Dallas, TX

Other

Posted 3 days ago

Job description

Machine Learning Engineer

We are seeking a Machine Learning Engineer to design and develop robust analytics models using statistical and machine learning algorithms. In this role, you will work closely with product and engineering teams to solve complex business problems, identify data-driven opportunities, and create personalized experiences for customers. You will be responsible for building end-to-end machine learning solutions, implementing models in production, and working with various data frameworks and tools such as Python, Spark, and Databricks.

Key Responsibilities
  • Analyze use cases and design appropriate analytics models using statistical and machine learning algorithms tailored to specific business requirements.
  • Develop machine learning algorithms to drive personalized customer experiences and provide actionable business insights.
  • Apply expertise in data mining and machine learning techniques, including forecasting, prediction, segmentation, recommendation, and fraud detection.
Data Engineering and Preparation
  • Extend and augment company data with third-party data to enrich analytics capabilities.
  • Enhance data collection procedures to include necessary information for building analytics systems.
  • Prepare raw data for analysis, including cleaning, imputing missing values, and standardizing data formats using Python data frameworks (e.g., Pandas, NumPy).
Machine Learning Model Implementation
  • Implement machine learning models, considering both performance and scalability using tools like PySpark in Databricks.
  • Design and build infrastructure to facilitate large-scale data analytics and experimentation.
  • Work with tools like Jupyter Notebooks for data exploration and model development.
What We're Looking For
  • Educational Background: Undergraduate or Graduate degree in Computer Science, Mathematics, Physics, or related fields. A PhD is preferred but not necessary.
  • Experience:
    • At least 5 years of experience in data analytics, with a strong understanding of core statistical algorithms such as classification and regression analysis.
    • High-level knowledge of analytics use cases such as language analysis, assortment optimization, promotional planning, dynamic pricing, markdown optimization, labor scheduling, and optimization.
  • Technical Skills:
    • Strong experience with Python-based machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch).
    • Proficiency in using analytics platforms like Databricks for large-scale data processing.
    • At least 4 years of continuous experience with Spark, particularly PySpark implementation.
    • Hands-on experience with data processing and analysis tools such as Pandas, NumPy, and Jupyter Notebooks.

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