Fraud AI/ML Data Scientist

Guidehouse is hiring an on-site Fraud AI/ML Data Scientist in Washington, DC to help stakeholders turn fraud and anomaly challenges into production-ready AI and advanced analytics. The role focuses on operationalizing models and analytics in an AWS data lakehouse, using Generative AI and foundation models alongside NLP and search capabilities to improve investigative workflows.

What you’ll do

• 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 and analytical approaches for search, discovery, anomaly detection, fraud detection, and insight generation across structured and unstructured data.
• Build 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.
• Apply Generative AI and foundation models to enhance detection workflows, including alert enrichment, case summarization, contextual analysis, evidence synthesis, pattern explanation, investigative hypothesis generation, and analyst decision support.
• Develop NLP and semantic search capabilities, including entity resolution and relationship analytics to support information retrieval and identification of suspicious connections.
• Use document-based data, including OCR/ICR outputs, metadata, images, extracted fields, and free text, to support analytics, search, anomaly detection, and fraud analysis.
• Collaborate with data engineers, cloud engineers, investigators, and business stakeholders to integrate models and analytics into production using AWS-native services.
• Operationalize data science solutions in an AWS data lakehouse, including data preparation, feature engineering, model training, inference, monitoring, and analytics.
• Create model evaluation frameworks covering confidence and risk scoring, explainability, traceability, and human-in-the-loop review for transparent, actionable outputs suitable for investigative and operational use.
• Evaluate models using precision, recall, F1 score, false-positive rate, detection rate, ranking quality, and business impact.
• Support dashboards, reporting, alerting, and investigative workflows that drive operational insights and informed decision-making.
• Work within an Agile delivery model, contributing to sprint planning, experimentation, model iteration, and incremental delivery.
• Communicate findings, risk indicators, model limitations, and recommendations clearly to technical and non-technical audiences.
• Contribute to solution design, proposal support, technical documentation, and thought leadership across AI, analytics, anomaly detection, and fraud detection.

What you’ll bring

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
• Minimum of 4 years of experience in data science, machine learning, artificial intelligence, or applied analytics roles.
• U.S. Citizenship required and ability to obtain and maintain a Public Trust clearance.
• Experience developing machine learning solutions in one or more areas such as NLP, semantic search or information retrieval, entity resolution or relationship modeling, anomaly or outlier detection, fraud/risk/waste/abuse/suspicious-pattern detection, and graph analytics or network analysis.
• Experience with anomaly and fraud detection techniques including classification, clustering, isolation-based methods, autoencoders, time-series analysis, behavioral analytics, link analysis, rules-based detection, or ensemble modeling.
• Demonstrated competency with AWS-native services, including Amazon S3, AWS Glue, AWS Lake Formation, Amazon Athena, Amazon EMR, Amazon Redshift, Amazon SageMaker, Amazon Bedrock, AWS Lambda, AWS Step Functions, Amazon OpenSearch Service, Amazon ECS or Amazon EKS, and Amazon CloudWatch.
• Demonstrated competency with AWS data lakehouse architectures, including Amazon S3-based data lakes, Apache Iceberg (or similar open table formats), centralized metadata catalogs, governed access, schema evolution, partitioning, and scalable query and transformation patterns.
• Experience using Generative AI, large language models, or foundation models for anomaly and fraud detection analysis (alert enrichment, investigative summarization, contextual reasoning, evidence synthesis, pattern explanation, and analyst assistance).
• Experience working with large-scale structured and unstructured data, especially document-based datasets such as text, PDFs, images, extracted fields, and metadata.
• Strong Python skills for ML and data science (including Pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow) and strong SQL skills.
• Experience building scalable data processing and ML pipelines using AWS-native services and open-source frameworks such as Apache Spark.
• Experience integrating models into production (APIs, batch pipelines, event-driven workflows, streaming pipelines, or embedded analytics applications).
• Understanding of model evaluation, validation, monitoring, drift detection, bias assessment, explainability, and performance measurement.
• Understanding of operational challenges in anomaly and fraud detection including class imbalance, changing patterns, false positives, data leakage, concept drift, and feedback-loop design.
• Ability to apply responsible AI, privacy, security, and governance practices when using Generative AI with sensitive or regulated data.
• Strong communication skills to translate analytical outputs and detected risks into actionable insights.
• Experience working in cross-functional, matrixed teams in an Agile environment.

Nice to have

• Experience delivering production-grade anomaly detection, fraud detection, waste and abuse analytics, risk scoring, or investigative decision support.
• Experience using Amazon Bedrock or comparable enterprise Generative AI capabilities for analytics, anomaly detection, fraud investigation, or case-management workflows.
• Experience implementing Retrieval-Augmented Generation (RAG), prompt engineering, agentic workflows, guardrails, foundation-model evaluation, and responsible AI controls.
• Experience building AI-enabled search solutions, including semantic search, hybrid search, document retrieval, embeddings, and ranking models on AWS-native services.
• Experience with multimodal data processing across text, images, documents, and metadata.
• Familiarity with OCR/ICR and document intelligence pipelines using Amazon Textract and Amazon Bedrock.
• Experience using Amazon SageMaker across the ML lifecycle (feature engineering, development, training, deployment, and monitoring).
• Experience with Amazon OpenSearch Service and/or graph databases/frameworks for relationship mapping, link analysis, and suspicious-network detection.
• Experience developing explainable AI solutions such as confidence scoring, reason codes, feature attribution, and evidence traceability.
• Experience designing analytics dashboards, alert-management interfaces, investigative reporting, or case-triage solutions.
• Knowledge of AWS security and governance practices (IAM, encryption, logging, data classification, least-privilege access, and handling sensitive or regulated information).
• Previous experience supporting federal clients or working in regulated environments.
• Consulting experience or client-facing delivery role strongly preferred.
• Experience supporting training, user enablement, or adoption of analytics capabilities across teams.
• Familiarity with graph-based analytics, ontology-driven models, knowledge graphs, or relationship mapping.

Location and schedule

• On-site in Washington, DC 20001
• Travel: up to 10%

Compensation

USD 80,000 - 133,000 per year (annual).

Benefits

• Medical, Rx, Dental & Vision Insurance
• Personal and Family Sick Time & Company Paid Holidays
• Parental Leave
• 401(k) Retirement Plan
• Group Term Life and Travel Assistance
• Voluntary Life and AD&D Insurance
• Health Savings Account, Health Care & Dependent Care Flexible Spending Accounts
• Transit and Parking Commuter Benefits
• Short-Term & Long-Term Disability
• Tuition Reimbursement, Personal Development, Certifications & Learning Opportunities
• Employee Referral Program
• Corporate Sponsored Events & Community Outreach
• Care.com annual membership
• Employee Assistance Program
• Supplemental Benefits via Corestream (Critical Care, Hospital Indemnity, Accident Insurance, Legal Assistance and ID theft protection, etc.)
• Position may be eligible for a discretionary variable incentive bonus