Fraud AI/ML Data Scientist

Guidehouse

. Partner with stakeholders to define and deliver AI, machine learning, and advanced analytics use cases .

Posted 10/16/2026full-timeRemote • United StatesMid-LevelSenior💰 $80,000 - $133,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and operationalizing machine learning models, particularly in NLP, anomaly detection, and fraud detection, while leveraging AWS-native services and data lakehouse architectures. Strong ability to communicate complex analytical findings to diverse audiences and collaborate effectively in Agile environments.

Highest-signal resume keywords

Machine Learning Model DevelopmentNatural Language Processing (NLP)AWS-Native Services ProficiencyAnomaly Detection TechniquesData Science Solution Operationalization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData SciencePythonSQLGenerative AIStatistical AnalysisGraph-Based TechniquesModel EvaluationData Processing PipelinesDocument-Based Data Analysis

Soft Skills

Strong Communication SkillsCollaborationAgile Methodology Experience

Tools & Technologies

AWS S3AWS GlueAmazon SageMakerApache SparkAmazon RedshiftAmazon EMRAmazon OpenSearch ServiceAWS LambdaAWS Step FunctionsAmazon CloudWatch

Industry Keywords

Artificial IntelligenceData LakehouseAnomaly DetectionFraud DetectionEntity ResolutionSemantic SearchStatistical TechniquesBehavioral AnalyticsCross-Functional CollaborationPublic Trust Clearance

Tech Stack

Tools & technologies

Amazon RedshiftApacheAWSCloudNumpyPandasPythonPyTorchScikit-LearnSparkSQLTensorflow

About the role

Key responsibilities & impact

Partner with stakeholders to define and deliver AI, machine learning, and advanced analytics use cases
Translate business needs into scalable data science solutions
Design and develop machine learning models for search, discovery, anomaly detection, fraud detection, and insight generation
Develop anomaly and fraud detection analyses using supervised, unsupervised, semi-supervised, statistical, and graph-based techniques
Use Generative AI and foundation models for alert enrichment, case summarization, contextual analysis, evidence synthesis, pattern explanation, hypothesis generation, and analyst decision support
Build NLP, semantic search, entity resolution, and relationship analytics capabilities
Leverage document-based data including OCR/ICR outputs, metadata, images, extracted fields, and free text
Collaborate with data engineers, cloud engineers, investigators, and business stakeholders to integrate models into production using AWS-native services
Develop and operationalize data science solutions within an AWS data lakehouse
Build model evaluation frameworks, confidence and risk scoring, explainability, traceability, and human-in-the-loop review approaches
Evaluate models using precision, recall, F1 score, false-positive rate, detection rate, ranking quality, and business impact
Support dashboards, reporting, alerting, and investigative workflows
Operate in an Agile delivery model through sprint planning, experimentation, model iteration, and incremental delivery
Communicate findings, risk indicators, model limitations, and recommendations to technical and non-technical audiences
Contribute to solution design, proposal support, technical documentation, and thought leadership

Requirements

What you’ll need

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
Ability to obtain and maintain a Public Trust clearance
Experience developing and applying machine learning models in NLP, semantic search/information retrieval, entity resolution/relationship modeling, anomaly/outlier detection, fraud/risk/waste/abuse detection, or graph/network analysis
Experience designing or implementing anomaly and fraud detection analyses using classification, clustering, isolation-based techniques, autoencoders, time-series analysis, behavioral analytics, link analysis, rules-based detection, or ensemble modeling
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/EKS, and Amazon CloudWatch
Competency with AWS data lakehouse architectures, Apache Iceberg or similar open table formats, metadata catalogs, governed data access, schema evolution, partitioning, data quality, and scalable query/transformation patterns
Competency using Generative AI, large language models, or foundation models for anomaly and fraud detection
Experience with large-scale structured and unstructured document-based data
Experience using metadata, engineered features, embeddings, event/transaction histories, and relationship data
Strong Python proficiency, including Pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow
Strong SQL skills
Experience building scalable data processing and machine learning pipelines using AWS-native services and Apache Spark
Experience integrating models into production through 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 anomaly and fraud detection challenges 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 with sensitive or regulated data
Strong communication skills and ability to translate analytical outputs into actionable insights
Experience working in cross-functional, matrixed teams in an Agile environment

Benefits

Comp & perks

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
Flexible benefits package