Fraud AI/ML Data Scientist

Guidehouse•San Antonio, TX

2d•$80,000 - $133,000

About The Position

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 for 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, and use Generative AI and foundation models to enhance detection workflows and support investigators. Build natural language processing, semantic search, entity resolution, and relationship analytics capabilities. Use document-based data, including OCR/ICR outputs, metadata, images, extracted fields, and free text, to support analytics and fraud analysis. Collaborate with data engineers, cloud engineers, investigators, and business stakeholders to integrate solutions into production environments using AWS-native services. Develop and operationalize data science solutions in an AWS data lakehouse, including data preparation, feature engineering, model training, inference, monitoring, and analytics. Develop evaluation frameworks, confidence and risk scoring, explainability, traceability, and human-in-the-loop review approaches. Support dashboards, reporting, alerting, and investigative workflows; work in an Agile delivery model; communicate findings and recommendations to technical and non-technical audiences; and contribute to solution design, proposal support, technical documentation, and thought leadership. The position requires U.S. citizenship and the ability to obtain and maintain a Public Trust clearance. The annual salary range is $80,000–$133,000. Compensation decisions depend on factors including skills, experience, training, clearances, licensure, certifications, and organizational needs. The position may be eligible for a discretionary variable incentive bonus. Travel required: up to 10%.

Requirements

Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
At least 4 years of experience in data science, machine learning, artificial intelligence, or applied analytics roles.
U.S. citizenship and ability to obtain and maintain a Public Trust clearance.
Experience developing and applying machine learning models in one or more areas: NLP, semantic search or information retrieval, entity resolution or relationship modeling, anomaly or outlier detection, fraud/risk/waste/abuse or suspicious-pattern detection, or graph analytics/network analysis.
Competency 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.
Competency 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 EKS, and Amazon CloudWatch.
Competency with AWS data lakehouse architectures, including S3-based data lakes, Apache Iceberg or similar open table formats, centralized metadata catalogs, governed access, schema evolution, partitioning, data quality, and scalable query and transformation patterns.
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 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 relationship data for analytics and modeling.
Strong Python proficiency for data science and machine learning, including libraries such as Pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow, and strong SQL skills.
Experience building scalable data processing and machine learning pipelines using AWS-native services and open-source frameworks such as 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 operational 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 when using Generative AI with sensitive or regulated data.
Strong communication skills and ability to translate analytical outputs, detected risks, and model results into actionable insights.
Experience working in cross-functional, matrixed teams in an Agile environment.

Nice To Haves

Experience developing production-grade anomaly detection, fraud detection, waste and abuse analytics, risk-scoring, or investigative decision-support solutions.
Experience using Amazon Bedrock or comparable enterprise Generative AI capabilities for analytics, anomaly detection, fraud investigation, or case-management workflows.
Experience implementing RAG, prompt engineering, agentic workflows, guardrails, foundation-model evaluation, and responsible AI controls for enterprise use cases.
Experience building AI-enabled search solutions, including semantic search, hybrid search, document retrieval, embeddings, and ranking models using AWS-native services.
Experience with multimodal data processing involving text, images, documents, metadata, and other data types.
Familiarity with OCR/ICR and document intelligence pipelines using services such as Amazon Textract and Amazon Bedrock.
Experience using Amazon SageMaker for feature engineering, model development, training, deployment, monitoring, and machine learning lifecycle management.
Experience using Amazon OpenSearch Service, graph databases, or graph-processing frameworks for relationship mapping, link analysis, and suspicious-network detection.
Experience developing explainable AI solutions, including confidence scoring, reason codes, feature attribution, evidence traceability, and analyst-friendly explanations.
Experience designing analytics dashboards, alert-management interfaces, investigative reporting, or case-triage solutions for end users.
Knowledge of AWS security and governance practices, including 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 experience in a client-facing delivery role is 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.

Responsibilities

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 for search, discovery, anomaly detection, fraud detection, and insight generation across structured and unstructured data.
Develop anomaly and fraud 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 for alert enrichment, case summarization, contextual analysis, evidence synthesis, pattern explanation, investigative hypothesis generation, and analyst decision support.
Build NLP, semantic search, entity resolution, and relationship analytics capabilities for information retrieval and identification of suspicious patterns and connections.
Use document-based data, including OCR/ICR outputs, metadata, images, extracted fields, and free text, for 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 using AWS-native services.
Develop and operationalize data science solutions in 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.
Evaluate anomaly and fraud detection 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.
Contribute to Agile sprint planning, experimentation, model iteration, and incremental solution delivery.
Communicate findings, risk indicators, model limitations, and recommendations 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.

Benefits

Medical, prescription, dental, and 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 and dependent care flexible spending accounts
Transit and parking commuter benefits
Short-term and long-term disability
Tuition reimbursement
Personal development, certifications, and learning opportunities
Employee referral program
Corporate-sponsored events and community outreach
Care.com annual membership
Employee assistance program
Supplemental benefits, including critical care, hospital indemnity, accident insurance, legal assistance, and ID theft protection
Potential eligibility for a discretionary variable incentive bonus