Fraud AI/ML Data Scientist

This is a fully remote position, open to applicants in United States.

📋 Description

• Collaborate with stakeholders to identify and implement AI, machine learning, and advanced analytics use cases.

• Convert business requirements into scalable data science solutions.

• Create and design machine learning models for search, discovery, anomaly detection, fraud detection, and generating insights.

• Conduct analyses for anomaly and fraud detection utilizing supervised, unsupervised, semi-supervised, statistical, and graph-based methods.

• Employ Generative AI and foundational models for enriching alerts, summarizing cases, performing contextual analysis, synthesizing evidence, explaining patterns, generating hypotheses, and supporting analyst decisions.

• Develop capabilities in NLP, semantic search, entity resolution, and relationship analytics.

• Utilize document-based data including OCR/ICR outputs, metadata, images, extracted fields, and free text.

• Work alongside data engineers, cloud engineers, investigators, and business stakeholders to implement models in production using AWS-native services.

• Build and operationalize data science solutions within an AWS data lakehouse.

• Establish model evaluation frameworks, confidence and risk scoring, explainability, traceability, and human-in-the-loop review methods.

• Assess models based on precision, recall, F1 score, false-positive rate, detection rate, ranking quality, and business impact.

• Maintain dashboards, reporting, alerting, and investigative workflows.

• Function in an Agile delivery framework through sprint planning, experimentation, model iteration, and incremental delivery.

• Articulate findings, risk indicators, model limitations, and recommendations to both technical and non-technical audiences.

• Contribute to solution design, proposal support, technical documentation, and thought leadership.

⛳️ Requirements

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

• At least 4 years of experience in data science, machine learning, artificial intelligence, or applied analytics roles.

• U.S. Citizenship is required.

• Ability to obtain and maintain a Public Trust clearance.

• Experience in 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.

• Proven experience in designing or implementing analyses for anomaly and fraud detection using classification, clustering, isolation-based techniques, autoencoders, time-series analysis, behavioral analytics, link analysis, rules-based detection, or ensemble modeling.

• Proficiency in AWS-native 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/EKS, and Amazon CloudWatch.

• Familiarity 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.

• Experience in using Generative AI, large language models, or foundational models for anomaly and fraud detection.

• Background in handling large-scale structured and unstructured document-based data.

• Knowledge of using metadata, engineered features, embeddings, event/transaction histories, and relationship data.

• Strong proficiency in Python, including libraries such as Pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow.

• Excellent SQL skills.

• Experience in constructing scalable data processing and machine learning pipelines utilizing AWS-native services and Apache Spark.

• Ability to integrate models into production via 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.

• Awareness of the challenges associated with anomaly and fraud detection, including class imbalance, changing patterns, false positives, data leakage, concept drift, and feedback-loop design.

• Capability to apply responsible AI, privacy, security, and governance practices with sensitive or regulated data.

• Strong communication skills and the ability to translate analytical outputs into actionable insights.

• Experience collaborating in cross-functional, matrixed teams within an Agile environment.

🏝️ 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.

• Flexible benefits package.