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Fraud Detection Machine Learning Jobs in Silver Spring, MD

Samprasoft

Machine Learning Engineer

Mclean, VA

GCP/AWS Machine Learning Engineer Freddie Mac iLab is currently looking for Machine Learning ... object detection, image generation), machine translation, language modeling, rankings and ...

Navstar

Machine Learning Engineer

Centreville, VA

$102K - $144K/yr

Duties * Support development of computer vision and machine learning (ML) algorithms capable of object detection, classifying, localizing, and tracking objects of interest from a variety of ...

AI Squared

Machine Learning Engineer

Washington, DC · On-site

Machine Learning Engineer Washington, DC (Hybrid) About the Role: We are seeking a highly skilled ... Build robust model monitoring, logging, and alerting systems to track performance and detect drift.

Ai Squared

Machine Learning Engineer

Washington, DC

Machine Learning Engineer Washington, DC (Hybrid) About the Role: We are seeking a highly skilled ... Build robust model monitoring, logging, and alerting systems to track performance and detect drift.

AI Squared

Machine Learning Engineer

Washington, DC · On-site

Machine Learning Engineer Washington, DC (Hybrid) About the Role: We are seeking a highly skilled ... Build robust model monitoring, logging, and alerting systems to track performance and detect drift.

AI Squared

Machine Learning Engineer

Washington, DC

Machine Learning Engineer Washington, DC (Hybrid) About the Role: We are seeking a highly skilled ... Build robust model monitoring, logging, and alerting systems to track performance and detect drift.

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

As of Jun 14, 2026, the average hourly pay for fraud detection machine learning in Silver Spring, MD is $18.66, according to ZipRecruiter salary data. Most workers in this role earn between $15.38 and $19.90 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 Silver Spring, MD are hiring for Fraud Detection Machine Learning jobs? Cities near Silver Spring, MD with the most Fraud Detection Machine Learning job openings:
Fraud Detection Machine Learning jobs near you
Machine Learning Engineer

Machine Learning Engineer

Samprasoft

Mclean, VA

Other

Posted 3 days ago

Job description

GCP/AWS Machine Learning Engineer

Freddie Mac iLab is currently looking for Machine Learning Engineers in its Innovation Labs - Tech Strategy team. In this position, you will be responsible for taking on new initiatives to design, build, and deploy machine learning models in a client solution-oriented environment. As a member of our team, you will make an immediate impact on building and expanding current technology platforms across AWS and GCP.

What you will do:

  • Work within an agile team that includes members with cross-functional skills
  • Collaborate closely with other functional teams to design, build, test, and deploy AI solutions to address market needs
  • Design and implement Machine Learning algorithms and models into software solutions for our enterprise customers by using common machine learning frameworks, including establishing and training machine learning and deep learning models at scale for computer vision (image recognition, object detection, image generation), machine translation, language modeling, rankings and recommendations, speech recognition, etc.
  • Design, build, and implement cloud native applications using the GCP and AWS services, event streaming technologies, and various open source frameworks
  • Build distributed, scalable, and reliable data pipelines that ingest and process data at scale and in real-time to feed machine learning algorithms
  • Incorporate real-time data streams to produce highly predictive features in our models
  • Write understandable, testable, and secure code with an eye towards quality and maintainability.

What we are looking for:

  • At least a Bachelor's degree in Computer Science, Mathematics, related technical field or equivalent practical experience.
  • A blend of data engineering, machine learning, and product innovation skills that let you jump into a fast-paced environment and contribute on day one
  • Familiar with monitoring, deployment tools, platforms and Infrastructure as Code (IaC)
  • At least 5 years of experience designing and implementing software solutions for complex problems
  • At least 3 years of experience with cloud computing platform (AWS and GCP)
  • At least 3 years of experience with Cloud Native Architecture, Docker, Microservices, Kubernetes, EKE/GKE, serverless computing, etc.
  • At least 3 years of experience as a data engineer, with large-scale data ecosystems including data lake, data management, governance and the integration of structured and unstructured data to generate insights leveraging cloud-based platforms
  • Experience with building and deploying ML-based solutions and proficiency in common machine learning frameworks such as TensorFlow, XGBoost, scikit-learn, Pytorch and ONNX and programming languages (Python, Java, Go, etc.

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