Lead Enterprise Fraud Risk Analyst - 90411601 - Washington

Amtrak•Washington, DC

3d•$94,300 - $122,256•Onsite

About The Position

The Lead Enterprise Fraud Risk Analyst is responsible for executing advanced data analytics to identify, analyze, and mitigate internal and external fraud risks. This role will conduct investigative analyses, generate insights on emerging fraud patterns, and partner with cross‑functional teams to improve fraud detection, prevention, and reporting. This role will also execute fraud incident management activities and assist with development and documentation of incident management processes to mature Amtrak’s fraud risk management program. The Analyst is a member of the Fraud Risk Management Team and is expected to lead preliminary investigations which may be referred to other investigative teams. This position supports Amtrak's Enterprise Fraud Risk Management Program and is responsible for identifying, analyzing, and investigating a broad range of fraud risks across the organization, including procurement fraud, financial statement fraud, bribery and corruption, payment card fraud, and other internal and external fraud schemes. The ideal candidate brings broad fraud analytics and investigative experience across multiple fraud domains, rather than expertise in a single specialty such as AML, financial crimes, or cybersecurity.

Requirements

Bachelor’s Degree or equivalent combination of education, training and/or relevant experience. Plus 6 years of relevant work experience.
Minimum 3 years of hands-on experience in an advanced analytical role using Data Science or Machine Learning.
Advanced proficiency in SQL and demonstrated ability to work with large complex datasets.
Strong analytical and problem-solving skills, with the ability to relate multiple datasets from complex business processes, simplify and transform them into meaningful features, ready to be consumed in predictive models.
Demonstrated experience in data visualization tools such as Tableau, Power BI, or similar.
Fraud expertise, including knowledge of common fraud schemes, risk indicators, control weaknesses, and industry best practices for detection and prevention.
Previous experience investigating large, complex datasets, with the ability to identify anomalies, connect disparate data points, perform root‑cause analysis, and develop actionable insights.
Excellent oral and written communication skills, with the ability to translate complex technical concepts for end‑users while engaging deeply with technical investigative teams.
Ability to work independently and collaboratively in a fast-paced, dynamic environment.

Nice To Haves

Bachelor’s Degree or equivalent combination of education, training and/or relevant experience. Plus 8 years of relevant work experience.
Degree in Computer Science, Mathematics, Finance, or a related field.
Practical, hands‑on experience applying AI/ML techniques
Preferred Proficiency in Python or R for data manipulation, modeling, and automation.
Preferred Previous experience with Payment Card Fraud schemes and detection methods.
Preferred Familiarity with calculating and communicating metrics for management reporting.
Preferred Familiarity with risk management frameworks.

Responsibilities

Extract, aggregate, normalize, and analyze data from multiple internal and external sources to support fraud monitoring and investigations.
Transform unstructured data using AI/ML techniques to create structured, analysis ready datasets.
Develop a strong understanding of systems, data structures, and business processes through documentation review and collaboration with data owners and operational teams.
Analyze suspicious transactions, behavioral patterns, and anomalies to identify potential fraud risks and emerging schemes.
Test fraud related controls using data analysis to assess compliance with applicable laws, regulations, and internal policies
Identify opportunities to enhance detective analytics and preventative fraud rules.
Develop and deliver visualizations and reports that communicate fraud trends, risks, and key performance indicators to senior leadership.
Conduct investigative research to validate suspicious activity identified by business units or external partners.
Support implementation and execution of fraud case management cycle processes, and develop complete investigative case packages and written intelligence products such as investigative memoranda.
Contribute to the development and maintenance of SOPs, monitoring procedures, case management workflows, and continuous improvement of detection methodologies.
Perform ad hoc statistical, investigative, and operational analytics to support fraud investigations.
Conduct investigative research to validate suspicious activity identified by business units or external partners.
Translate analytical findings into actionable recommendations and ensure analytical processes are documented, repeatable, auditable, and aligned with quality standards