Top AI & Machine Learning Jobs

Lead and deliver large-scale AI/ML programs and the next-generation Generative AI platform. Build and grow a world-class TPM discipline, manage cross-functional delivery, mitigate technical risk, and drive execution across product, engineering, design, and data science to achieve business impact in regulated environments.

Lead development of valuations and credit models for Business Cards & Payments. Partner with cross-functional teams to design, train, validate, monitor, and implement ML solutions using Python, Spark, H2O, and cloud infrastructure. Translate technical work into business impact and support tooling and analytics for credit programs.

Lead and execute compliance assessments of scripts and models (CoDIR), identify high-risk scripts, prepare and report findings, monitor governance inventory, and explore GenAI/automation to improve processes.

Lead development and evaluation of statistical and machine learning models to assess credit risk and challenge production models. Use Python, cloud, and big-data tools to analyze large numeric and textual datasets, partner with cross-functional credit risk and product teams, translate technical findings into business actions, and drive model validation, backtesting, and innovation.

Build, validate, and deploy machine learning models across the credit card lifecycle using big data tools. Partner with cross-functional teams to turn insights from large numeric and textual datasets into business impact.

Lead development and deployment of machine learning models across the credit card lifecycle. Partner with cross-functional teams to build data-driven products, analyze large numeric and textual datasets, validate and implement models, and translate technical insights into business impact.

The role involves partnering with data scientists and engineers to develop machine learning models, evaluate model risk in fraud detection, and translate complex data insights into business goals.

The Data Scientist Manager leads a team in assessing model risks, developing machine learning models, and presenting insights, while leveraging various technologies.