Fraud AI​/ML Data Scientist

Job Family:
Data Science & Analysis

Travel Required:

Up to 10%

Clearance Required:

Ability to Obtain Public Trust

Partner with stakeholders to define and deliver AI, machine learning, and advanced analytics use cases, translating business needs into scalable data science solutions.

Design and develop machine learning models and analytical approaches to support search, discovery, anomaly detection, fraud detection, and insight generation across structured and unstructured data.

Develop anomaly and fraud detection analyses using supervised, unsupervised, semi-supervised, statistical, and graph-based techniques, including outlier detection, behavioral profiling, risk scoring, pattern detection, and relationship analysis.

Use Generative AI and foundation models to enhance anomaly and fraud detection workflows through alert enrichment, case summarization, contextual analysis, evidence synthesis, pattern explanation, investigative hypothesis generation, and analyst decision support.

Build and implement natural language processing, semantic search, entity resolution, and relationship analytics capabilities to enable advanced information retrieval and identification of suspicious patterns and connections.

Leverage document-based data, including OCR/ICR outputs, metadata, images, extracted fields, and free text, to support downstream analytics, search, anomaly detection, and fraud analysis.

Collaborate with data engineers, cloud engineers, investigators, and business stakeholders to integrate models and analytical capabilities into production environments using AWS-native services.

Develop and operationalize data science solutions within an AWS data lakehouse, including scalable data preparation, feature engineering, model training, inference, monitoring, and analytics.

Develop model evaluation frameworks, confidence and risk scoring, explainability, traceability, and human-in-the-loop review approaches to ensure AI outputs are transparent, actionable, and suitable for investigative and operational use.

Evaluate anomaly and fraud detection models using appropriate measures, including precision, recall, F1 score, false-positive rate, detection rate, ranking quality, and business impact.

Support the development of dashboards, reporting, alerting, and investigative workflows that drive operational insights and informed decision-making.

Operate within an Agile delivery model, contributing to sprint planning, experimentation, model iteration, and incremental solution delivery.

Communicate findings, risk indicators, model limitations, and recommendations clearly to technical and non-technical audiences, including client stakeholders.

Contribute to solution design, proposal support, technical documentation, and thought leadership in AI, analytics, anomaly detection, and fraud detection.

Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.

A minimum of 4 years of experience in data science, machine learning, artificial intelligence, or applied analytics roles.

U.S. Citizenship required and the ability to obtain and maintain a Public Trust clearance.

Experience developing and applying machine learning models in one or more of the following areas:
Natural Language Processing (NLP);
Semantic search or information retrieval;
Entity resolution or relationship modeling;
Anomaly or outlier detection;
Fraud, risk, waste, abuse, or suspicious-pattern detection;
Graph analytics or network analysis.

Skills and experience designing or implementing anomaly and fraud detection analyses using methods such as classification; clustering; isolation-based techniques; autoencoders; time-series analysis; behavioral analytics; link…