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Fraud Detection Machine Learning Jobs in Ashburn, VA

Varsity Tutors

Machine Learning Tutor

Rockville, MD · Remote

$18 - $40/hr

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

Varsity Tutors

Machine Learning Tutor

Washington, DC · Remote

$18 - $40/hr

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

Varsity Tutors

Machine Learning Tutor

Leesburg, VA · Remote

$18 - $40/hr

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

Varsity Tutors

Machine Learning Tutor

Fairfax, VA · Remote

$18 - $40/hr

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

Elder Research

Senior AI/ML Lead

Arlington, VA · On-site

This role provides technical leadership across the lifecycle of machine learning models used to detect risk, identify anomalous activity, and strengthen fraud prevention capabilities. The ideal ...

Elder Research

Senior AI/ML Lead

Arlington, VA · Hybrid

This role provides technical leadership across the lifecycle of machine learning models used to detect risk, identify anomalous activity, and strengthen fraud prevention capabilities. The ideal ...

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Fraud Detection Machine Learning information

See Ashburn, VA salary details

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

As of Aug 23, 2026, the average hourly pay for fraud detection machine learning in Ashburn, VA is $18.46, according to ZipRecruiter salary data. Most workers in this role earn between $15.24 and $19.66 per hour, depending on experience, location, and employer.

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 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 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 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 popular job titles related to Fraud Detection Machine Learning jobs in Ashburn, VA?

For Fraud Detection Machine Learning jobs in Ashburn, VA, the most frequently searched job titles are:

What job categories do people searching Fraud Detection Machine Learning jobs in Ashburn, VA look for?

The top searched job categories for Fraud Detection Machine Learning jobs in Ashburn, VA are:

Fraud Detection Machine Learning jobs near you

Data Scientist (Fraud Analytics & Investigative Support) with Security Clearance

Praescient Analytics

Fairfax, VA • On-site

Other

Retirement, PTO

Re-posted 22 days ago

Job description

Location: Remote (Occasional Travel May Be Required) Clearance: Ability to obtain and maintain a Public Trust Position Overview Praescient Analytics is seeking multiple Data Scientists to support advanced fraud analytics and investigative initiatives for a federal oversight organization. These positions will develop, test, validate, and deploy innovative analytical solutions that help identify fraud, waste, abuse, and mismanagement across large-scale federal benefit programs and other government-funded initiatives. Working as part of a multidisciplinary analytics team, Data Scientists will leverage statistical analysis, machine learning, data visualization, entity resolution, and risk modeling techniques to transform large, diverse datasets into actionable intelligence supporting investigators, auditors, and government decision-makers. The ideal candidate is a technically strong, mission-focused data scientist who enjoys solving complex analytical problems while collaborating with investigators, business analysts, data engineers, forensic accountants, and government stakeholders. Key Responsibilities * Develop, test, validate, and maintain fraud detection and program integrity analytics. * Design analytical rules and methodologies to identify fraud indicators, anomalies, suspicious activity, and emerging risks. * Perform exploratory data analysis, feature engineering, model development, validation, and performance evaluation. * Analyze structured and unstructured data from multiple public, non-public, commercial, financial, and government data sources. * Collaborate with Data Engineers to prepare and optimize data for analytics. * Support entity resolution, anomaly detection, predictive analytics, and risk scoring initiatives. * Produce dashboards, reports, visualizations, and analytical products that support investigative decision-making. * Document analytical methodologies, assumptions, validation results, and technical findings. * Participate in Agile delivery activities including sprint planning, demonstrations, peer reviews, and iterative model development. Required Qualifications * Must have experience with Fraud Analysis * Three (3) or more years of professional experience in data science, applied analytics, machine learning, statistics, fraud analytics, or a related quantitative field. * Strong programming experience using Python and SQL. * Experience developing and validating analytical models. * Experience analyzing structured and unstructured datasets. * Experience documenting analytical methodologies and technical findings. * Strong analytical reasoning and problem-solving skills. * Excellent written and verbal communication skills. Preferred Qualifications Preference will be given to candidates with experience in one or more of the following: * Fraud detection, fraud prevention, financial crime analytics, or program integrity. * Federal benefit programs, grants, loans, healthcare, unemployment insurance, emergency assistance, disaster relief, or other public-sector programs. * Risk modeling, anomaly detection, entity resolution, predictive analytics, and statistical modeling. * Cloud analytics environments such as Azure Databricks, Microsoft SQL Server, Microsoft Fabric, Azure Data Lake Storage (ADLS), Power BI, Git repositories, or Lakehouse architectures. * Working with public, non-public, commercial, financial, or cross-agency datasets. * Data visualization and dashboard development. * Agile software development and analytics teams. * Enterprise data governance, metadata management, and data quality best practices. What We're Looking For We're looking for curious, collaborative data scientists who enjoy using data to solve challenging fraud detection problems. The ideal candidate combines strong analytical skills with a passion for supporting government oversight and investigative missions through innovative, data-driven solutions. What you can expect from us: * Real opportunity for career growth in an environment where your achievements will be celebrated * Constant collaboration with numerous teams to ensure client success * A team that respects and embraces your ideas and expertise * Coworkers that are motivated by pursuing excellence, rather than the prospect of personal gain * A workplace dedicated to supporting and bettering public safety and government agencies Benefits: * Competitive salary based on qualifications and experience * Comprehensive, Company paid healthcare for you (We pay your premiums and deductibles) * 401(k) with company match * Travel & performance incentives * 3 weeks paid time off (plus Federal Holidays) * $5K annual training allowance * $500 book allowance * Tuition reimbursement program Praescient Analytics is an Equal Employment Opportunity employer. Employment decisions are based on merit, qualifications, experience, performance, business needs, and applicable contract requirements. Praescient does not unlawfully discriminate or provide disparate treatment based on race, ethnicity, color, religion, sex, national origin, age, disability, veteran status, genetic information, or any other status protected by applicable law. Praescient Analytics acknowledges the applicable clause and provision updates implementing Executive Order 14398, Addressing DEI Discrimination by Federal Contractors, and the related FAR/RFO updates, including FAR 52.222-90 where applicable. Praescient does not engage in racially discriminatory DEI activities, including disparate treatment based on race or ethnicity in recruitment, hiring, promotion, contracting, program participation, training, mentoring, leadership development, or allocation of company resources. Praescient’s employment and contracting decisions are made based on merit, qualifications, experience, performance, business needs, and applicable contract requirements. Applicants selected will be subject to a government security investigation and must meet eligibility requirements for access to classified information. US Citizenship Required Interested Candidates: Please forward your resume to and please visit our website to apply online at www.praescientanalytics.applicantstack.com/x/openings. Apply Now © 2025, Praescient Analytics. All Rights Reserved. (703) 739-2110 8280 Willow Oaks Corporate Dr., Suite 610 Fairfax, VA 22031 (703) 739-2110 8280 Willow Oaks Corporate Dr., Suite 610 Fairfax, VA 22031 © 2025, Praescient Analytics. All Rights Reserved.

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