Fraud AI/ML Data Scientist

Guidehouse Inc.; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

Guidehouse Inc. seeks a data science professional to translate business needs into scalable ML solutions across structured and unstructured data. You will partner with stakeholders to deliver AI use cases and implement anomaly and fraud detection capabilities using AWS-native services.

Responsibilities span model development, NLP, semantic search, and explainability within an AWS data lakehouse, with opportunities to lead analytics initiatives in a dynamic team. U.S.

Qualifications

• U.S. Citizenship required and ability to obtain Public Trust clearance.
• 4+ years of experience in data science, ML, or applied analytics.
• Experience building ML solutions for NLP, semantic search, entity resolution, anomaly and fraud detection.

Responsibilities

• Partner with stakeholders to define AI/ML use cases and translate business needs into scalable data science solutions.
• Design and develop ML models for search, discovery, anomaly, and fraud detection across structured and unstructured data.
• Develop anomaly and fraud analyses using supervised, unsupervised, semi-supervised, and graph-based techniques.
• Apply Generative AI and foundation models to enhance anomaly and fraud workflows.
• Build NLP, semantic search, entity resolution, and relationship analytics capabilities for advanced information retrieval.
• Leverage OCR/ICR, metadata, images, and free text for downstream analytics and fraud analysis.
• Collaborate with data engineers and cloud engineers to deploy models in production using AWS services.
• Develop data science solutions within an AWS data lakehouse including feature engineering, training, inference, and monitoring.
• Create evaluation frameworks with explainability, risk scoring, and human-in-the-loop review.
• Evaluate models with precision, recall, F1, FP rate, and business impact.
• Support dashboards, alerting, and investigative workflows for operational insights.
• Work in an Agile environment with sprint planning and iterative model delivery.
• Communicate findings clearly to technical and non-technical audiences.
• Contribute to solution design, technical documentation, and thought leadership in AI/analytics.

Skills

NLP

Semantic search

Entity resolution

Graph analytics

Anomaly detection

Fraud detection

AWS SageMaker

MLOps

Data lakehouse

Time-series analytics

Education

Bachelor's or Master's in Data Science/CS/Statistics/Math/Engineering

Tools

SageMaker

Redshift

Athena

Glue

Lake Formation

OpenSearch

ECS/EKS

Apache Iceberg

Job description

Job Family : Data Science & Analysis

Travel Required : Up to 10%

Clearance Required : Ability to Obtain Public Trust

What You Will 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 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.

What You Will Need:

• 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 analysis, rules-based detection, or ensemble modeling.
• Demonstrated competency working with AWS-native data, analytics, AI, and machine learning services, such as 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 working with AWS data lakehouse architectures, including Amazon S3-based data lakes, Apache Iceberg or similar open table formats, centralized metadata catalogs, governed data access, schema evolution, partitioning, data quality, and scalable query and transformation patterns.
• Demonstrated competency using Generative AI, large language models, or foundation models for anomaly and fraud detection analysis, including alert enrichment, investigative summarization, contextual reasoning, evidence synthesis, pattern explanation, and analyst assistance.
• Experience working with large-scale structured and unstructured data, particularly document-based datasets such as text, PDFs, images, extracted fields, and metadata.
• Experience using metadata, engineered features, embeddings, event or transaction histories, and r