Top AI & Machine Learning Jobs

The Data Scientist will analyze data to develop machine learning models and analytical solutions for business problems in Corporate and Institutional Banking.

Build and monitor AML detection models using statistical and machine learning techniques on customer, account, and transaction data. Prepare datasets, run experiments, collaborate with stakeholders and governance, present results to business leaders, and assist model implementation and monitoring.

Leads emerging fraud risk detection for credit card and lending products. Applies data science, machine learning, anomaly detection, behavioral modeling, and network analysis to identify threats, investigate fraud patterns, validate hypotheses, and recommend controls. Builds scalable analytical pipelines using large structured and unstructured datasets, partners with fraud, risk, product, and technology teams, and influences strategic decisions through executive-level insights and presentations.

Build, deploy, and support production ML infrastructure and large-scale data pipelines. Optimize ML workflows for performance and reliability, troubleshoot production issues, document systems, and participate in on-call rotations and deployments.

Lead people-analytics projects: analyze large HR datasets using statistical methods and ML (SAS/Python/R), build models and visualizations, deliver insights to stakeholders, and partner with data engineers and architects to operationalize solutions.

This role involves developing AI/ML architectures, resolving complex issues, and partnering with stakeholders to enhance business outcomes through technology and data.

Leads fraud strategy and portfolio analytics initiatives using large structured and unstructured datasets. Develops machine learning algorithms, anomaly detection models, and statistical analyses to predict business outcomes and optimize products and processes. Directs data gathering, processing, mining, and analytical experiments, then presents insights through visualization and data storytelling. Collaborates with data architects, analysts, engineers, and visualization experts to deliver data-driven solutions.

Build anomaly detection metrics and monitoring tools to identify fraud; analyze large structured and unstructured datasets to detect suspicious activity and trends; develop ML algorithms and run experiments to optimize detection; visualize and present insights; partner with data architects, engineers, and cross-functional teams to improve fraud mitigation processes.

Manage development teams and application support for AI systems; oversee multiple application programming, installation, maintenance, and release projects; ensure quality, compliance, and alignment with business requirements; coach and develop staff and drive Agile delivery, process improvement, and system testing.

Lead design and execution of scalable MLOps testing and automation strategies. Build test harnesses, validate controls, support test data/environment readiness, analyze defects and incidents, provide quality sign-off, and mentor QE teams to standardize MLOps quality practices across cloud and CI/CD environments.