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Fraud Detection Machine Learning Jobs in Silver Spring, MD

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 ...

ID.me

Senior Fraud Investigations Analyst

Mclean, VA · On-site

... detection tools, machine learning outputs, and risk-scoring systems to drive high-quality ... fraud cases • Develop and refine investigation playbooks and analytical approaches for ATO ...

ID.me

Senior Fraud Investigations Analyst

Mclean, VA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

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

Magnus Management Group LLC

Graph Data Scientist

Washington, DC · Remote

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

... machine learning to identify hidden relationships, organized fraud rings, and emerging fraud ... Must have a deep understanding of network typology, centrality measures, community detection, and ...

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All JobsFraud Detection Machine Learning JobsFraud Detection Machine Learning Jobs in Silver Spring, MD

Fraud Detection Machine Learning information

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

As of Aug 13, 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 job categories do people searching Fraud Detection Machine Learning jobs in Silver Spring, MD look for?

The top searched job categories for Fraud Detection Machine Learning jobs in Silver Spring, MD are:

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
Infographic showing various Fraud Detection Machine Learning job openings in Silver Spring, MD as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $38,816 per year, or $18.7 per hour.

Fraud Investigations Analyst

ID.me

Mclean, VA • On-site

Full-time

Re-posted yesterday

ID.me rating

5.6

Company rating: 5.6 out of 10

Based on 7 frontline employees who took The Breakroom Quiz

230th of 244 rated software companies

Job description

Fraud Investigations AnalystRole Overview

ID.me is looking for an Fraud Investigations Analyst to join our organization as an execution-focused individual contributor. This role centers on investigating account takeover (ATO) activity, analyzing fraud signals, and supporting detection efforts that protect millions of users.

We are seeking someone who goes beyond basic case handling and can analyze trends, identify patterns across multiple accounts, and leverage data to inform investigations. As an Analyst, you will work across fraud detection tools, datasets, and risk systems to uncover coordinated fraud activity and contribute to improving detection capabilities.

This role is based out of our McLean, VA office and requires full-time in-office attendance.

Responsibilities
  • Investigate account takeover (ATO) activity using fraud indicators, behavioral signals, and transaction data
  • Analyze patterns across multiple accounts to identify coordinated or emerging fraud trends (not just single-account reviews)
  • Use SQL, Python, or similar tools to query datasets and support investigations
  • Leverage fraud detection tools and risk-scoring systems to identify suspicious activity
  • Execute investigations and document findings, escalating higher-risk or complex cases as needed
  • Support tuning and optimization of fraud detection rules, alerts, and signal usage
  • Partner with cross-functional teams to improve fraud detection coverage and data quality
  • Monitor trends in ATO activity and contribute to ongoing detection strategy improvements
  • Leverage AI/LLM tools to accelerate investigations, including querying data, identifying patterns, and summarizing fraud activity across accounts
Basic Qualifications
  • Bachelor's degree from an accredited institution; preferred fields include quantitative disciplines such as Economics, Computer Science, Statistics, Mathematics, or similar
  • 2+ years of experience in fraud investigations, threat intelligence, cybersecurity, or risk management, with exposure to account takeover (ATO)
  • 1+ years of experience using SQL, Python, or similar tools to analyze fraud data or investigate trends
  • 1+ years of hands-on experience using fraud detection tools, machine learning models, or risk-scoring methodologies
  • 1+ years of experience interpreting fraud indicators, behavioral signals, or transaction monitoring data
  • Familiarity with using AI/LLM tools (e.g., ChatGPT, Claude) to support data analysis or investigative workflows
Preferred Qualifications
  • Experience working at a fintech company, technology company, or reputable financial institution
  • Experience analyzing organized fraud rings or large-scale fraud patterns
  • Familiarity with machine learning-driven fraud detection systems or AI-assisted analysis

What ID.me employees say

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Hours and flexibility

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