... AML, and customer engagement. • Serve as technical authority for model architecture, feature ... validation, explainability, bias testing, and audit readiness. • Collaborate with product and ...
... AML, and customer engagement. • Serve as technical authority for model architecture, feature ... validation, explainability, bias testing, and audit readiness. • Collaborate with product and ...
... AML, and customer engagement. * Serve as technical authority for model architecture, feature ... Oversee model validation, explainability, bias testing, and audit readiness. * Collaborate with ...
... AML, and customer engagement. * Serve as technical authority for model architecture, feature ... Oversee model validation, explainability, bias testing, and audit readiness. * Collaborate with ...
... AML, and customer engagement. * Serve as technical authority for model architecture, feature ... Oversee model validation, explainability, bias testing, and audit readiness. * Collaborate with ...
... AML, and customer engagement. * Serve as technical authority for model architecture, feature ... Oversee model validation, explainability, bias testing, and audit readiness. * Collaborate with ...
... AML, and customer engagement. * Serve as technical authority for model architecture, feature ... Oversee model validation, explainability, bias testing, and audit readiness. * Collaborate with ...
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... AML, and customer engagement. * Serve as technical authority for model architecture, feature ... Oversee model validation, explainability, bias testing, and audit readiness. * Collaborate with ...
... AML, and customer engagement. * Serve as technical authority for model architecture, feature ... Oversee model validation, explainability, bias testing, and audit readiness. * Collaborate with ...
... AML, and customer engagement. * Serve as technical authority for model architecture, feature ... Oversee model validation, explainability, bias testing, and audit readiness. * Collaborate with ...
Aml Model Validation information
What are the key skills and qualifications needed to thrive as an AML model validation analyst, and why are they important?
To excel in AML Model Validation, you typically need a strong background in quantitative analysis, statistics, and experience with anti-money laundering regulations, often supported by a degree in finance, mathematics, or a related field. Familiarity with statistical software (such as SAS, R, or Python), model validation frameworks, and knowledge of regulatory guidelines like those from the OCC or FFIEC are important. Strong analytical thinking, attention to detail, and clear communication skills set outstanding professionals apart in this role. These competencies are crucial for ensuring AML models are accurate, compliant, and effective in detecting suspicious financial activities.
What is AML model validation?
AML model validation is the process of evaluating and testing anti-money laundering (AML) models to ensure they are accurate, effective, and compliant with regulatory standards. This involves examining the model’s design, data inputs, performance metrics, and overall effectiveness in detecting suspicious activities. Regular validation helps to identify weaknesses, reduce false positives or negatives, and ensure that the model adapts to evolving risk scenarios. Financial institutions are required by regulators to validate their AML models regularly to mitigate risks and maintain robust compliance programs.
What are some common challenges faced by professionals in AML model validation roles, and how can they be addressed?
Professionals in AML Model Validation often encounter challenges such as ensuring models remain effective against evolving financial crime techniques and managing the complexity of regulatory expectations. They must regularly update and back-test models to address changes in transaction patterns and compliance requirements, which can be resource-intensive. Collaboration with data scientists, risk management teams, and compliance officers is crucial for interpreting results and implementing improvements. Staying current with regulatory guidance and industry best practices helps address these challenges and supports career advancement in this dynamic field.
What is the difference between Aml Model Validation vs Aml Analyst?
| Aspect | Aml Model Validation | Aml Analyst |
|---|---|---|
| Certifications | AML certifications, model validation training | AML certifications, compliance training |
| Work Environment | Model validation teams, risk management departments | Compliance departments, financial institutions |
| Primary Focus | Validating AML models, ensuring accuracy and effectiveness | Monitoring transactions, investigating suspicious activities |
| Industry Usage | Financial institutions, banks, fintechs | Financial institutions, banks, regulatory agencies |
While both roles operate within AML frameworks, Aml Model Validation focuses on testing and validating AML models to ensure they work effectively, whereas Aml Analysts handle daily transaction monitoring and investigations. The validation role emphasizes model accuracy and compliance, while analysts focus on detecting and reporting suspicious activities.
What are popular job titles related to Aml Model Validation jobs in Nevada? For Aml Model Validation jobs in Nevada, the most frequently searched job titles are:
What job categories do people searching Aml Model Validation jobs in Nevada look for? The top searched job categories for Aml Model Validation jobs in Nevada are:
- Model Risk Manager
- Financial Risk Management
- Governance Risk Compliance Internship
- Internship Vendor Risk Analyst
- Entrylevel Governance Risk Compliance
- Bank Risk Management
- Senior Remote Risk Management
- Cybersecurity Governance Risk Compliance
- Entry Level Governance Risk Compliance
- Remote Project Risk Management
What cities in Nevada are hiring for Aml Model Validation jobs? Cities in Nevada with the most Aml Model Validation job openings:
Job description
Job Summary:
Credit One Bank is a data-driven financial services company based in Las Vegas. The Assistant Vice President of Artificial Intelligence is responsible for leading the delivery and execution of AI and machine learning capabilities within a regulated banking environment, focusing on fraud prevention, credit risk management, and customer experience personalization.
Responsibilities:
• Lead development and deployment of AI/ML and Generative AI solutions for fraud detection, credit scoring, underwriting, AML, and customer engagement.
• Serve as technical authority for model architecture, feature engineering, training pipelines, and inference services.
• Manage and mentor AI Engineers and ML practitioners; provide code and design reviews.
• Implement AIOps/MLOps and model governance practices aligned with banking regulations and internal Model Risk Management (MRM) standards.
• Partner with Risk, Compliance, Legal, Cybersecurity, and Data teams to ensure Responsible AI adoption.
• Oversee model validation, explainability, bias testing, and audit readiness.
• Collaborate with product and business leaders to translate financial use cases into scalable AI solutions.
Qualifications:
Required:
• Lead development and deployment of AI/ML and Generative AI solutions for fraud detection, credit scoring, underwriting, AML, and customer engagement.
• Serve as technical authority for model architecture, feature engineering, training pipelines, and inference services.
• Manage and mentor AI Engineers and ML practitioners; provide code and design reviews.
• Implement AIOps/MLOps and model governance practices aligned with banking regulations and internal Model Risk Management (MRM) standards.
• Partner with Risk, Compliance, Legal, Cybersecurity, and Data teams to ensure Responsible AI adoption.
• Oversee model validation, explainability, bias testing, and audit readiness.
• Collaborate with product and business leaders to translate financial use cases into scalable AI solutions.
• Machine Learning & Modeling: Supervised, unsupervised, reinforcement learning; Deep learning (CNNs, RNNs, Transformers); Natural Language Processing (NLP) & LLMs; Generative AI (diffusion models, fine-tuning, RAG); AI Engineering & MLOps.
• AI Engineering & MLOps: Model training, deployment, monitoring, and retraining; Feature stores, vector databases, and model registries; CI/CD pipelines for ML (MLOps); GPU/accelerator compute architectures.
• Cloud & Infrastructure: Azure AI, Azure ML, AWS Sagemaker, or Google Vertex AI; Kubernetes, containerization, microservices; Data platforms (Databricks, Snowflake, Synapse).
• Responsible AI & Governance: Model explainability (SHAP, LIME); Fairness, bias detection, model risk controls; Privacy-preserving ML techniques (differential privacy, federated learning).
• Programming & Tooling: Python, PyTorch, TensorFlow, JAX; LangChain, semantic search, vector embeddings; Prompt engineering & LLM orchestration frameworks.
• Excellent communication, problem-solving, and project management skills.
• Ability to collaborate effectively and follow up ensure achievement of deadlines, outcomes and results.
• Demonstrate company core values of excellence, ownership, collaboration, and integrity.
Preferred:
• Bachelor’s degree in Computer Science, Engineering, Data Science, or related field.
• 5-8 + years of experience in AI/ML or data science.
• Experience working with large-scale financial or transactional data is preferred.
Company:
Credit One Bank is a financial services company that offers credit cards, credit score tracking, and fraud protection services. Founded in 1984, the company is headquartered in Las Vegas, USA, with a team of 1001-5000 employees. The company is currently Late Stage.
Credit One Bank is a data-driven financial services company based in Las Vegas. The Assistant Vice President of Artificial Intelligence is responsible for leading the delivery and execution of AI and machine learning capabilities within a regulated banking environment, focusing on fraud prevention, credit risk management, and customer experience personalization.
Responsibilities:
• Lead development and deployment of AI/ML and Generative AI solutions for fraud detection, credit scoring, underwriting, AML, and customer engagement.
• Serve as technical authority for model architecture, feature engineering, training pipelines, and inference services.
• Manage and mentor AI Engineers and ML practitioners; provide code and design reviews.
• Implement AIOps/MLOps and model governance practices aligned with banking regulations and internal Model Risk Management (MRM) standards.
• Partner with Risk, Compliance, Legal, Cybersecurity, and Data teams to ensure Responsible AI adoption.
• Oversee model validation, explainability, bias testing, and audit readiness.
• Collaborate with product and business leaders to translate financial use cases into scalable AI solutions.
Qualifications:
Required:
• Lead development and deployment of AI/ML and Generative AI solutions for fraud detection, credit scoring, underwriting, AML, and customer engagement.
• Serve as technical authority for model architecture, feature engineering, training pipelines, and inference services.
• Manage and mentor AI Engineers and ML practitioners; provide code and design reviews.
• Implement AIOps/MLOps and model governance practices aligned with banking regulations and internal Model Risk Management (MRM) standards.
• Partner with Risk, Compliance, Legal, Cybersecurity, and Data teams to ensure Responsible AI adoption.
• Oversee model validation, explainability, bias testing, and audit readiness.
• Collaborate with product and business leaders to translate financial use cases into scalable AI solutions.
• Machine Learning & Modeling: Supervised, unsupervised, reinforcement learning; Deep learning (CNNs, RNNs, Transformers); Natural Language Processing (NLP) & LLMs; Generative AI (diffusion models, fine-tuning, RAG); AI Engineering & MLOps.
• AI Engineering & MLOps: Model training, deployment, monitoring, and retraining; Feature stores, vector databases, and model registries; CI/CD pipelines for ML (MLOps); GPU/accelerator compute architectures.
• Cloud & Infrastructure: Azure AI, Azure ML, AWS Sagemaker, or Google Vertex AI; Kubernetes, containerization, microservices; Data platforms (Databricks, Snowflake, Synapse).
• Responsible AI & Governance: Model explainability (SHAP, LIME); Fairness, bias detection, model risk controls; Privacy-preserving ML techniques (differential privacy, federated learning).
• Programming & Tooling: Python, PyTorch, TensorFlow, JAX; LangChain, semantic search, vector embeddings; Prompt engineering & LLM orchestration frameworks.
• Excellent communication, problem-solving, and project management skills.
• Ability to collaborate effectively and follow up ensure achievement of deadlines, outcomes and results.
• Demonstrate company core values of excellence, ownership, collaboration, and integrity.
Preferred:
• Bachelor’s degree in Computer Science, Engineering, Data Science, or related field.
• 5-8 + years of experience in AI/ML or data science.
• Experience working with large-scale financial or transactional data is preferred.
Company:
Credit One Bank is a financial services company that offers credit cards, credit score tracking, and fraud protection services. Founded in 1984, the company is headquartered in Las Vegas, USA, with a team of 1001-5000 employees. The company is currently Late Stage.
About Credit One Bank
Sourced by ZipRecruiter
Industry
Finance and insurance
Company size
501 - 1,000 Employees
Headquarters location
Las Vegas, NV, US
Year founded
1984